Splitting Long or Ill-formed Input for Robust Spoken-language Translation

Splitting Long or Ill-formed Input for Robust Spoken-language Translation Osamu FURUSE t, Setsuo YAMADA, Kazuhide YAMAMOTO ATR Interpreting Telecommunications Research Laboratories 2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan furuse~cslab, kecl. ntt. co. jp, {syamada, yamamoto}@itl, atr. co. jp Abstract This paper proposes an input-splitting method for translating spoken-language which includes many long or ill-formed expressions. The pro- posed method splits input into well-balanced translation units based on a semantic distance calculation. The splitting is performed dur- ing left-to-right parsing, and does not degrade translation efficiency. The complete translation result is formed by concatenating the partial translation results of each split unit. The pro- posed method can be incorporated into frame- works like TDMT, which utilize left-to-right parsing and a score for a substructure. Experi- mental results show that the proposed method gives TDMT the following advantages: (1) elim- ination of null outputs, (2) splitting of utter- ances into sentences, and (3) robust translation of erroneous speech recognition results. 1 Introduction A spoken-language translation system requires the ability to treat long or ill-formed input. An utterance as input of a spoken-language trans- lation system, is not always one well-formed sentence. Also, when treating an utterance in speech translation, the speech recognition result which is the input of the translation component, might be corrupted even though the input utter- ance is well-formed. Such a misrecognized result can cause a parsing failure, and consequently, no translation output would be produced. Further- more, we cannot expect that a speech recogni- tion result includes punctuation marks such as a comma or a period between words, which are useful information for parsing. 1 As a solution for treating long input, long- sentence splitting techniques, such as that of tCurrent affiliation is NTT Communication Science Laboratories. 1 Punctuation marks are not used in translation input in this paper. Kim (1994), have been proposed. These tech- niques, however, use many splitting rules writ- ten manually and do not treat ill-formed in- put. Wakita (1997) proposed a robust transla- tion method which locally extracts only reliable parts, i.e., those within the semantic distance threshold and over some word length. This technique, however, does not split input into units globally, or sometimes does not output any translation result. This paper proposes an input-splitting method for robust spoken-language translation. The proposed method splits input into well- balanced translation units based on a seman- tic distance calculation. The complete trans- lation result is formed by concatenating the partial translation results of each split unit. The proposed method can be incorporated into frameworks that utilize left-to-right parsing and a score for a substructure, In fact, it has been added to Transfer-Driven Machine Trans- lation (TDMT), which was proposed for efficient and robust spoken-language translation (Fu- ruse, 1994; Furuse, 1996). The splitting is per- formed during TDMT's left-to-right chart pars- ing strategy, and does not degrade translation efficiency. The proposed method gives TDMT the following advantages: (1) elimination of null outputs, (2) splitting of utterances into sen- tences, and (3) robust translation of erroneous …

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