Conference Paper

Rule Filtering by Pattern for Efficient Hierarchical Translation.

Conference: EACL 2009, 12th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, March 30 - April3, 2009, Athens, Greece
Source: DBLP

ABSTRACT We describe refinements to hierarchical translation search procedures intended to reduce both search errors and memory us- age through modifications to hypothesis expansion in cube pruning and reductions in the size of the rule sets used in transla- tion. Rules are put into syntactic classes based on the number of non-terminals and the pattern, and various filtering strate- gies are then applied to assess the impact on translation speed and quality. Results are reported on the 2008 NIST Arabic-to- English evaluation task. lation. Memory usage can be reduced in cube pruning (Chiang, 2007) through smart memoiza- tion, and spreading neighborhood exploration can be used to reduce search errors. However, search errors can still remain even when implementing simple phrase-based translation. We describe a 'shallow' search through hierarchical rules which greatly speeds translation without any effect on quality. We then describe techniques to analyze and reduce the set of hierarchical rules. We do this based on the structural properties of rules and develop strategies to identify and remove redun- dant or harmful rules. We identify groupings of rules based on non-terminals and their patterns and assess the impact on translation quality and com- putational requirements for each given rule group. We find that with appropriate filtering strategies rule sets can be greatly reduced in size without im- pact on translation performance.

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    ABSTRACT: This paper presents a novel filtration criterion to restrict the rule extraction for the hierarchical phrase-based translation model, where a bilingual but relaxed well-formed dependency restriction is used to filter out bad rules. Furthermore, a new feature which describes the regularity that the source/target dependency edge triggers the target/source word is also proposed. Experimental results show that, the new criteria weeds out about 40% rules while with translation performance improvement, and the new feature brings another improvement to the baseline system, especially on larger corpus.
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    ABSTRACT: Shallow-n grammars (de Gispert et al., 2010) were introduced to reduce over-generation in the Hiero translation model (Chiang, 2005) resulting in much faster decoding and restricting reordering to a desired level for specific language pairs. However, Shallow-n grammars require parameters which cannot be directly optimized using minimum error-rate tuning by the decoder. This paper introduces some novel improvements to the translation model for Shallow-n grammars. We introduce two rules: a BITG-style reordering glue rule and a simpler monotonic concatenation rule. We use separate features for the new rules in our log-linear model allowing the decoder to directly optimize the feature weights. We show this formulation of Shallow-n hierarchical phrase-based translation is comparable in translation quality to full Hiero-style decoding (without shallow rules) while at the same time being considerably faster.
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