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language_model/srilm-1.7.3/man/html/nbest-optimize.1.html
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<! $Id: nbest-optimize.1,v 1.31 2019/09/09 22:35:37 stolcke Exp $>
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<HTML>
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<HEADER>
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<TITLE>nbest-optimize</TITLE>
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<BODY>
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<H1>nbest-optimize</H1>
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<H2> NAME </H2>
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nbest-optimize - optimize score combination for N-best word error minimization
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<H2> SYNOPSIS </H2>
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<PRE>
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<B>nbest-optimize</B> [ <B>-help</B> ] <I>option</I> ... [ <I>scoredir</I> ... ]
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</PRE>
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<H2> DESCRIPTION </H2>
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<B> nbest-optimize </B>
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reads a set of N-best lists, additional score files, and corresponding
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reference transcripts and optimizes the score combination weights
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so as to minimize the word error of a classifier that performs
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word-level posterior probability maximization.
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The optimized weights are meant to be used with
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<A HREF="nbest-lattice.1.html">nbest-lattice(1)</A>
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and the
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<B> -use-mesh </B>
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option,
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or the
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<B> nbest-rover </B>
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script (see
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<A HREF="nbest-scripts.1.html">nbest-scripts(1)</A>).
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<B> nbest-optimize </B>
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determines both the best relative weighting of knowledge source scores
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and the optimal
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<B> -posterior-scale </B>
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parameter that controls the peakedness of the posterior distribution.
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<P>
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Alternatively,
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<B> nbest-optimize </B>
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can also optimize weights for a standard, 1-best hypothesis rescoring that
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selects entire (sentence) hypotheses
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(<B>-1best</B><B></B><B></B>
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option).
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In this mode sentence-level error counts may be read from external files,
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or computed on the fly from the reference strings.
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The weights obtained are meant to be used for N-best list rescoring with
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<B> rescore-reweight </B>
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(see
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<A HREF="nbest-scripts.1.html">nbest-scripts(1)</A>).
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<P>
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A third optimization criterion is the BLEU score used in machine translation.
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This also requires the associated scores to be read from external files.
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<P>
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One of three optimization algorithms are available:
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<DL>
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<DT>1.
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<DD>
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The default optimization method is gradient descent on a smoothed (sigmoidal)
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approximation of the true 0/1 word error function (Katagiri et al. 1990).
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Therefore, the result can only be expected to be a
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<I> local </I>
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minimum of the error surface.
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(A more global search can be attempted by specifying different starting
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points.)
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Another approximation is that the error function is computed assuming a fixed
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multiple alignment of all N-best hypotheses and the reference string,
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which tends to slightly overestimate the true pairwise error between any
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single hypothesis and the reference.
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<DT>2.
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<DD>
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An alternative search strategy uses a simplex-based "Amoeba" search on
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the (non-smoothed) error function (Press et al. 1988).
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The search is restarted multiple times to avoid local minima.
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<DT>3.
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<DD>
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A third algorithm uses Powell search (Press et al. 1988)
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on the (non-smoothed) error function.
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</DD>
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</DL>
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<H2> OPTIONS </H2>
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<P>
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Each filename argument can be an ASCII file, or a
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compressed file (name ending in .Z or .gz), or ``-'' to indicate
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stdin/stdout.
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<DL>
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<DT><B> -help </B>
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<DD>
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Print option summary.
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<DT><B> -version </B>
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<DD>
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Print version information.
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<DT><B>-debug</B><I> level</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Controls the amount of output (the higher the
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<I>level</I>,<I></I><I></I><I></I>
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the more).
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At level 1, error statistics at each iteration are printed.
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At level 2, word alignments are printed.
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At level 3, full score matrix is printed.
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At level 4, detailed information about word hypothesis ranking is printed
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for each training iteration and sample.
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<DT><B>-nbest-files</B><I> file-list</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Specifies the set of N-best files as a list of filenames.
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Three sets of standard scores are extracted from the N-best files:
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the acoustic model score, the language model score, and the number of
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words (for insertion penalty computation).
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See
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<A HREF="nbest-format.5.html">nbest-format(5)</A>
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for details.
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<BR>
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In BLEU optimization mode, since there is no acoustic score, the
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position
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of the first score is taken by the "ac-replacement" score, which can be
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any score used by the machine translation system.
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A typical example is a score measuring
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word order distortion between the source and target languages.
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<DT><B>-xval-files</B><I> file-list</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Use the N-best files listed in
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<I> file-list </I>
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for cross-validation.
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Optimization stops once no improvement is found on the cross-validation set
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(while the direction of the search is obtained from the training set).
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This is only supported with the gradient-descent and Amoeba optimization
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methods.
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<DT><B>-srinterp-format</B><I></I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Parse n-best list in SRInterp format, which has
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features and text in the same line. n-best optimize will also generate
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rover-control file in SRInterp format, where each line is in the form of:
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<PRE>
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<I>F1=V1</I> <I>F2=V2</I> ... <I>Fm=Vm</I> <I>W1</I> <I>W2</I> ...<I>Wn</I>
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</PRE>
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where
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<I> F1 </I>
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through
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<I> Fm </I>
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are feature names,
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<I> V1 </I>
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through
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<I> Vm </I>
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are feature values,
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<I> W1 </I>
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through
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<I> Wn </I>
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are words.
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Also generate SRInterp control file, in the format of:
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<PRE>
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<I>F1:S1</I> <I>F2:S2</I> ... <I>Fm:Sm</I>
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</PRE>
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where
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<I> S1 </I>
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through
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<I> Sm </I>
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are scaling factors (weights) for feature
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<I> F1 </I>
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through
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<I> Fm </I>
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.
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<DT><B>-refs</B><I> references</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Specifies the reference transcripts.
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Each line in
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<I> references </I>
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must contain the sentence ID (the last component in the N-best filename
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path, minus any suffixes) followed by zero or more reference words.
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<DT><B>-insertion-weight</B><I> W</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Weight insertion errors by a factor
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<I>W</I>.<I></I><I></I><I></I>
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This may be useful to optimize for keyword spotting tasks where
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insertions have a cost different from deletion and substitution errors.
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<DT><B>-word-weights</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Read a table of words and weights from
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<I>file</I>.<I></I><I></I><I></I>
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Each word error is weighted according to the word-specific weight.
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The default weight is 1, and used if a word has no specified weight.
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Also, when this option is used, substitution errors are counted
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as the sum of a deletion and an insertion error, as opposed to counting
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as 1 error as in traditional word error computation.
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<DT><B>-anti-refs</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Read a file of "anti-references" for use with the
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<B> -anti-ref-weight </B>
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option (see below).
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<DT><B>-anti-ref-weight</B><I> W</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Compute the hypothesis errors for 1-best optimization by adding the
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edit distance with respect to the "anti-references" times the weight
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<I> W </I>
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to the regular error count.
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If
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<I> W </I>
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is negative this will tend to generate hypotheses that are different from
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the anti-references (hence the name).
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<DT><B> -1best </B>
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<DD>
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Select optimization for standard sentence-level hypothesis selection.
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<DT><B> -1best-first </B>
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<DD>
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Optimized first using
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<B> -1best </B>
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mode, then switch to full optimization.
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This is an effective way to quickly bring the score weights near an
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optimal point, and then fine-tune them jointly with the posterior scale
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parameter.
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<DT><B>-errors</B><I> dir</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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In 1-best mode, optimize for error counts that are stored in separate files
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in directory
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<I>dir</I>.<I></I><I></I><I></I>
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Each N-best list must have a matching error counts file of the same
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basename in
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<I>dir</I>.<I></I><I></I><I></I>
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Each file contains 7 columns of numbers in the format
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<PRE>
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<I>wcr</I> <I>wer</I> <I>nsub</I> <I>ndel</I> <I>nins</I> <I>nerr</I> <I>nw</I>
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</PRE>
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Only the last two columns (number of errors and words, respectively) are used.
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<BR>
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If this option is omitted, errors will be computed from the N-best hypotheses
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and the reference transcripts.
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<DT><B>-bleu-counts</B><I> dir</I><B></B><I></I><B></B><I></I><B></B>
|
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<DD>
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Perform BLEU optimization, reading BLEU reference counts from directory
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<I>dir</I>.<I></I><I></I><I></I>
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Each N-best list must have a matching counts file of the same
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basename in
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<I>dir</I>,<I></I><I></I><I></I>
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containing the following information:
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<PRE>
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<I>N</I> <I>M</I> <I>L1</I> ... <I>LM</I>
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</PRE>
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where
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<I> N </I>
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is the number of hypotheses in the N-best list,
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<I> M </I>
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is the number of references for the utterance,
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and
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<I> L1 </I>
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through
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<I> LM </I>
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are the reference lengths (word counts) for each reference.
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Following this line, there are
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<I> N </I>
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lines of the form
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<PRE>
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<I>K</I> <I>C1</I> <I>C2</I> ... <I>Cm</I>
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</PRE>
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where
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<I> K </I>
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is the number of words in the hypothesis and
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<I> C1 </I>
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through
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<I> Cm </I>
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are the N-gram counts occurring in the references for each N-gram order
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1, ...,
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<I>m</I>.<I></I><I></I><I></I>
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Currently,
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<I> m </I>
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is limited to 4.
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<DT><B> -minimum-bleu-reference </B>
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<DD>
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Use shortest reference length to compute the BLEU brevity penalty.
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<DT><B> -closest-bleu-reference </B>
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<DD>
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Use closest reference length for each translation hypothesis to compute
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the BLEU brevity penalty.
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<DT><B> -average-bleu-reference </B>
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<DD>
|
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Use average reference length to compute the BLEU brevity penalty.
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<DT><B> -error-bleu-ratio R </B>
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<DD>
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Specifies the weight of error rate when combined with BLEU as optimization
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||||
objective: <I>(1-BLEU) + ERR x R</I>.
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<I> ERR </I>
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is error rate computed by #errors/#references.
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<DT><B>-max-nbest</B><I> n</I><B></B><I></I><B></B><I></I><B></B>
|
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<DD>
|
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Limits the number of hypotheses read from each N-best list to the first
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<I>n</I>.<I></I><I></I><I></I>
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<DT><B>-rescore-lmw</B><I> lmw</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Sets the language model weight used in combining the language model log
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probabilities with acoustic log probabilities.
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This is used to compute initial aggregate hypotheses scores.
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<DT><B>-rescore-wtw</B><I> wtw</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Sets the word transition weight used to weight the number of words relative to
|
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the acoustic log probabilities.
|
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This is used to compute initial aggregate hypotheses scores.
|
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<DT><B>-posterior-scale</B><I> scale</I><B></B><I></I><B></B><I></I><B></B>
|
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<DD>
|
||||
Initial value for scaling log posteriors.
|
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The total weighted log score is divided by
|
||||
<I> scale </I>
|
||||
when computing normalized posterior probabilities.
|
||||
This controls the peakedness of the posterior distribution.
|
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The default value is whatever was chosen for
|
||||
<B>-rescore-lmw</B>,<B></B><B></B><B></B>
|
||||
so that language model scores are scaled to have weight 1,
|
||||
and acoustic scores have weight 1/<I>lmw</I>.
|
||||
<DT><B> -combine-linear </B>
|
||||
<DD>
|
||||
Compute aggregate scores by linear combination, rather than log-linear
|
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combination.
|
||||
(This is appropriate if the input scores represent log-posterior probabilities.)
|
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<DT><B> -non-negative </B>
|
||||
<DD>
|
||||
Constrain search to non-negative weight values.
|
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<DT><B>-vocab</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Read the N-best list vocabulary from
|
||||
<I>file</I>.<I></I><I></I><I></I>
|
||||
This option is mostly redundant since words found in the N-best input
|
||||
are implicitly added to the vocabulary.
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||||
<DT><B> -tolower </B>
|
||||
<DD>
|
||||
Map vocabulary to lowercase, eliminating case distinctions.
|
||||
<DT><B> -multiwords </B>
|
||||
<DD>
|
||||
Split multiwords (words joined by '_') into their components when reading
|
||||
N-best lists.
|
||||
<DT><B>-multi-char</B><I> C</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Character used to delimit component words in multiwords
|
||||
(an underscore character by default).
|
||||
<DT><B> -no-reorder </B>
|
||||
<DD>
|
||||
Do not reorder the hypotheses for alignment, and start the alignment with
|
||||
the reference words.
|
||||
The default is to first align hypotheses by order of decreasing scores
|
||||
(according to the initial score weighting) and then the reference,
|
||||
which is more compatible with how
|
||||
<A HREF="nbest-lattice.1.html">nbest-lattice(1)</A>
|
||||
operates.
|
||||
<DT><B>-noise</B><I> noise-tag</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Designate
|
||||
<I> noise-tag </I>
|
||||
as a vocabulary item that is to be ignored in aligning hypotheses with
|
||||
each other (the same as the -pau- word).
|
||||
This is typically used to identify a noise marker.
|
||||
<DT><B>-noise-vocab</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Read several noise tags from
|
||||
<I>file</I>,<I></I><I></I><I></I>
|
||||
instead of, or in addition to, the single noise tag specified by
|
||||
<B>-noise</B>.<B></B><B></B><B></B>
|
||||
<DT><B>-hidden-vocab</B> file<B></B><B></B><B></B>
|
||||
<DD>
|
||||
Read a subvocabulary from
|
||||
<I> file </I>
|
||||
and constrain word alignments to only group those words that are either all
|
||||
in or outside the subvocabulary.
|
||||
This may be used to keep ``hidden event'' tags from aligning with
|
||||
regular words.
|
||||
<DT><B>-dictionary</B> file<B></B><B></B><B></B>
|
||||
<DD>
|
||||
Use word pronunciations listed in
|
||||
<I> file </I>
|
||||
to construct word alignments when building word meshes.
|
||||
This will use an alignment cost function that reflects the number of
|
||||
inserted/deleted/substituted phones, rather than words.
|
||||
The dictionary
|
||||
<I> file </I>
|
||||
should contain one pronunciation per line, each naming a word in the first
|
||||
field, followed by a string of phone symbols.
|
||||
<DT><B>-distances</B> file<B></B><B></B><B></B>
|
||||
<DD>
|
||||
Use the word distance matrix in
|
||||
<I> file </I>
|
||||
as a cost function for word alignments.
|
||||
Each line in
|
||||
<I> file </I>
|
||||
defines a row of the distance matrix.
|
||||
The first field contains the word that is the row index,
|
||||
followed by one or more word/number pairs, where the word represents the
|
||||
column index and the number the distance value.
|
||||
<DT><B>-init-lambdas</B><I> 'w1 w2 ...'</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Initialize the score weights to the values specified
|
||||
(zeros are filled in for missing values).
|
||||
The default is to set the initial acoustic model weight to 1,
|
||||
the language model weight from
|
||||
<B>-rescore-lmw</B>,<B></B><B></B><B></B>
|
||||
the word transition weight from
|
||||
<B>-rescore-wtw</B>,<B></B><B></B><B></B>
|
||||
and all remaining weights to zero initially.
|
||||
Prefixing a value with an equal sign (`=')
|
||||
holds the value constant during optimization.
|
||||
(All values should be enclosed in quotes to form a single command-line
|
||||
argument.)
|
||||
<BR>
|
||||
Hypotheses are aligned using the initial weights; thus, it makes sense
|
||||
to reoptimize with initial weights from a previous optimization in order
|
||||
to obtain alignments closer to the optimimum.
|
||||
<DT><B>-alpha</B><I> a</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Controls the error function smoothness;
|
||||
the sigmoid slope parameter is set to
|
||||
<I>a</I>.<I></I><I></I><I></I>
|
||||
<DT><B>-epsilon</B><I> e</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
The step-size used in gradient descent (the multiple of the gradient vector).
|
||||
<DT><B>-min-loss</B><I> x</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Sets the loss function for a sample effectively to zero when its value falls
|
||||
below
|
||||
<I>x</I>.<I></I><I></I><I></I>
|
||||
<DT><B>-max-delta</B><I> d</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Ignores the contribution of a sample to the gradient if the derivative
|
||||
exceeds
|
||||
<I>d</I>.<I></I><I></I><I></I>
|
||||
This helps avoid numerical problems.
|
||||
<DT><B>-maxiters</B><I> m</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Stops optimization after
|
||||
<I> m </I>
|
||||
iterations.
|
||||
In Amoeba search, this limits the total number of points in the parameter space
|
||||
that are evaluated.
|
||||
<DT><B>-max-bad-iters</B> n<B></B><B></B><B></B>
|
||||
<DD>
|
||||
Stops optimization after
|
||||
<I> n </I>
|
||||
iterations during which the actual (non-smoothed) error has not decreased.
|
||||
<DT><B>-max-amoeba-restarts</B> r<B></B><B></B><B></B>
|
||||
<DD>
|
||||
Perform only up to
|
||||
<I> r </I>
|
||||
repeated Amoeba searches.
|
||||
The default is to search until
|
||||
<I> D </I>
|
||||
searches give the same results, where
|
||||
<I> D </I>
|
||||
is the dimensionality of the problem.
|
||||
<DT><B>-max-time</B><I> T</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Abort search if new lower-error point isn't found in
|
||||
<I> T </I>
|
||||
seconds.
|
||||
<DT><B>-epsilon-stepdown</B><I> s</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
<DT><B>-min-epsilon</B><I> m</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
If
|
||||
<I> s </I>
|
||||
is a value greater than zero, the learning rate will be multiplied by
|
||||
<I> s </I>
|
||||
every time the error does not decrease after a number of iterations
|
||||
specified by
|
||||
<B>-max-bad-iters</B>.<B></B><B></B><B></B>
|
||||
Training stops when the learning rate falls below
|
||||
<I> m </I>
|
||||
in this manner.
|
||||
<DT><B>-converge</B><I> x</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Stops optimization when the (smoothed) loss function changes relatively by less
|
||||
than
|
||||
<I> x </I>
|
||||
from one iteration to the next.
|
||||
<DT><B> -quickprop </B>
|
||||
<DD>
|
||||
Use the approximate second-order method known as "QuickProp" (Fahlman 1989).
|
||||
<DT><B>-init-amoeba-simplex</B><I> 's1 s2 ...'</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Perform Amoeba simplex search.
|
||||
The argument defines the step size for the initial Amoeba simplex.
|
||||
One value for each non-fixed search dimension should be specified,
|
||||
plus optionally a value for the posterior scaling parameter
|
||||
(which is searched as an added dimension).
|
||||
<DT><B>-init-powell-range</B><I> 'a1</I><B>,</B><I>b1 a2</I><B>,</B><I>b2 ...'</I><B></B>
|
||||
<DD>
|
||||
Perform Powell search.
|
||||
The argment initializes the weight ranges for Powell search.
|
||||
One comma-separated pair of values for each search dimension should
|
||||
be specified. For each dimension, if the upper bound equals lower bound
|
||||
and initial lambda, that dimension will be fixed, even if not so specified by
|
||||
<B> -init-lambda . </B>
|
||||
<DT><B>-num-powell-runs</B><I> N</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Sets the number of random runs for quick Powell grid search
|
||||
(default value is 20).
|
||||
<DT><B> -dynamic-random-series </B>
|
||||
<DD>
|
||||
Use time and process ID to initialize seed for pseudo random series used
|
||||
in Powell search.
|
||||
This will make results unrepeatable but may yield better results through
|
||||
multiple trials.
|
||||
<DT><B>-print-hyps</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Write the best word hypotheses to
|
||||
<I> file </I>
|
||||
after optimization.
|
||||
<DT><B>-print-top-n</B><I> N</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Write out the top
|
||||
<I> N </I>
|
||||
rescored hypotheses.
|
||||
In this case
|
||||
<B> -print-hyps </B>
|
||||
specifies a directory (not a file)
|
||||
and one file per N-best list is generated.
|
||||
<DT><B> -print-unique-hyps </B>
|
||||
<DD>
|
||||
Eliminate duplicate hypotheses when writing out N-best hypotheses.
|
||||
<DT><B> -print-old-ranks </B>
|
||||
<DD>
|
||||
Output the original hypothesis ranks when writing out N-best hypotheses.
|
||||
<DT><B> -compute-oracle </B>
|
||||
<DD>
|
||||
Find the lowest error rate or the highest BLEU score achievable by choosing
|
||||
among all N-best hypotheses.
|
||||
<DT><B>-print-oracle-hyps</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Print output oracle hyps to
|
||||
<I>file</I>.<I></I><I></I><I></I>
|
||||
<DT><B>-write-rover-control</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
|
||||
<DD>
|
||||
Writes a control file for
|
||||
<B> nbest-rover </B>
|
||||
to
|
||||
<I>file</I>,<I></I><I></I><I></I>
|
||||
reflecting the names of the input directories and the optimized parameter
|
||||
values.
|
||||
The format of
|
||||
<I> file </I>
|
||||
is described in
|
||||
<A HREF="nbest-scripts.1.html">nbest-scripts(1)</A>.
|
||||
The file is rewritten for each new minimal error weight combination found.
|
||||
<BR>
|
||||
In BLEU optimization, the weight for the ac-replacement score will be written
|
||||
in the place of the posterior scale,
|
||||
since posterior scaling is not used in BLEU optimization.
|
||||
<DT><B> -skipopt </B>
|
||||
<DD>
|
||||
Skip optimization altogether, such as when only the
|
||||
<B> -print-hyps </B>
|
||||
function is to be exercised.
|
||||
<DT><B> -- </B>
|
||||
<DD>
|
||||
Signals the end of options, such that following command-line arguments are
|
||||
interpreted as additional scorefiles even if they start with `-'.
|
||||
<DT><I>scoredir</I> ...<I></I><I></I><I></I>
|
||||
<DD>
|
||||
Any additional arguments name directories containing further score files.
|
||||
In each directory, there must exist one file named after the sentence
|
||||
ID it corresponds to (the file may also end in ``.gz'' and contain compressed
|
||||
data).
|
||||
The total number of score dimensions is thus 3 (for the standard scores from
|
||||
the N-best list) plus the number of additional score directories specified.
|
||||
</DD>
|
||||
</DL>
|
||||
<H2> SEE ALSO </H2>
|
||||
<A HREF="nbest-lattice.1.html">nbest-lattice(1)</A>, <A HREF="nbest-scripts.1.html">nbest-scripts(1)</A>, <A HREF="nbest-format.5.html">nbest-format(5)</A>.
|
||||
<BR>
|
||||
S. Katagiri, C.H. Lee, & B.-H. Juang, "A Generalized Probabilistic Descent
|
||||
Method", in
|
||||
<I>Proceedings of the Acoustical Society of Japan, Fall Meeting</I>,
|
||||
pp. 141-142, 1990.
|
||||
<BR>
|
||||
S. E. Fahlman, "Faster-Learning Variations on Back-Propagation: An
|
||||
Empirical Study", in D. Touretzky, G. Hinton, & T. Sejnowski (eds.),
|
||||
<I>Proceedings of the 1988 Connectionist Models Summer School</I>, pp. 38-51,
|
||||
Morgan Kaufmann, 1989.
|
||||
<BR>
|
||||
W. H. Press, B. P. Flannery, S. A. Teukolsky, & W. T. Vetterling,
|
||||
<I>Numerical Recipes in C: The Art of Scientific Computing</I>,
|
||||
Cambridge University Press, 1988.
|
||||
<BR>
|
||||
<H2> BUGS </H2>
|
||||
Gradient-based optimization is not supported (yet) in 1-best or BLEU mode
|
||||
or in conjunction with the
|
||||
<B> -combine-linear </B>
|
||||
or
|
||||
<B> -non-negative </B>
|
||||
options;
|
||||
use simplex or Powell search instead.
|
||||
<BR>
|
||||
The N-best directory in the control file output by
|
||||
<B> -write-rover-control </B>
|
||||
is inferred from the
|
||||
first N-best filename specified with
|
||||
<B>-nbest-files</B>,<B></B><B></B><B></B>
|
||||
and will therefore only work if all N-best lists are placed in the same
|
||||
directory.
|
||||
<P>
|
||||
The
|
||||
<B> -insertion-weight </B>
|
||||
and
|
||||
<B> -word-weights </B>
|
||||
options only affect the word error computation, not the construction
|
||||
of hypothesis alignments.
|
||||
Also, they only apply to sausage-based, not 1-best error optimization.
|
||||
(1-best errors may be explicitly specified using the
|
||||
<B> -errors </B>
|
||||
option).
|
||||
<P>
|
||||
The
|
||||
<B> -anti-refs </B>
|
||||
and
|
||||
<B> -anti-ref-weight </B>
|
||||
options do not work for sausage-based or BLEU optimization.
|
||||
<H2> AUTHORS </H2>
|
||||
Andreas Stolcke <andreas.stolcke@microsoft.com>,
|
||||
Dimitra Vergyri <dverg@speech.sri.com>,
|
||||
Jing Zheng <zj@speech.sri.com>
|
||||
<BR>
|
||||
Copyright (c) 2000-2012 SRI International
|
||||
<BR>
|
||||
Copyright (c) 2012-2016 Andreas Stolcke
|
||||
<BR>
|
||||
Copyright (c) 2012-2016 Microsoft Corp.
|
||||
</BODY>
|
||||
</HTML>
|
||||
Reference in New Issue
Block a user