Parsing Japanese Spoken Sentences Based on HPSG

P a r s in g J a p a n e s e S p o k e n S e n te n c e s B a s e d on H PSG

K iyoshi KOGURE

ATR In te rp re tin g Telephony Research Laboratories

Sanpeidani, In u id a n i, Seika-Cho, Soraku-gun, Kyoto 619-02, Japan

kogure% atr-la. atr.junet@ uunet.uu.net

A B S T R A C T

An analysis method for J a p an e se spoken sentences based on HPSG has been developed. Any analysis module for the in te rp re ting telephony task requires the following capabilities: (i) the

module m ust be able to tre a t spoken-style sentences; and, (ii) the module m ust be able to take, as its input, lattice-like s tru c tu re s which include both correct and incorrect constituent candidates of a speech recognition module. To satisfy these re q u ir e m e n ts , an a n a ly s is m ethod has been developed, which consists of a g ra m m a r designed for tre a tin g spoken-style J a p an e se sentences and a parser designed for taking as its input speech recognition output lattices. The analysis module based on this method is used as p a rt of the N A D IN E (N atu ral Dialogue I n te rp re ta tio n Expert) system and the SL-TRANS (Spoken Language T r a n s la tio n ) system.

1. IN T R O D U C T I O N

An analysis module for a spoken sentence tra n sla tio n system, or an in te rp re tin g telephony system requires the following capabilities:

(i) the module m ust be able to tre a t spoken-style sentences; and,

(ii) the module m ust be able to accept not only strings but also lattice-like s tru c tu re s where the analysis module directly drives a speech recognition module (e.g., a phoneme or word recognition module but not a whole sentence recognition module) or where the analysis module takes as its in p u ts p a r t i a l speech rec o g n itio n r e s u l ts in c lu d in g b o th c o r r e c t a n d i n c o r r e c t s e n te n c e constituents.

To satisfy these req u irem en ts, an analysis method has been developed which consists of a g r a m m a r fram ew ork designed for tre a tin g spoken-style Ja p a n e s e sentences and a unification- based p a rs e r designed for tak in g as its input speech recognition re su lt lattices.

The g r a m m a r fram ew ork is unification-based lexico-syntactic and is essentially based on

H P S G U 0 1 and JPSG121. This is because:

(i) a lexico-syntactic approach is modular in the sense th a t most of the g ram m atical information is to be specified in descriptions of lexical items; and th a t it is therefore easy to extend a g ra m m a r

simply by adding new lexical items to the lexicon or adding new information to lexical items; and (ii) the J P S G f ra m e w o rk can e s s e n tia lly c a p tu r e c o n s tr a in t s b e tw e en c o m p le x p r e d i c a t e con stituents and th eir complements. This capability is im p o rta n t because spoken-style J a p a n e s e sentences often have complex predicate c onstituents.

The g r a m m a r fram ew ork is e x te n d e d from th ese g r a m m a t ic a l fra m e w o rk s by in tro d u c in g

featu res related to sem antic and prag m atic constra ints!12).

The p a rs e r developed is essentially based on the active c h a rt parsing a lgo rith m !11) because the alg orithm is as efficient as Earley's alg orith m !1) or any other CFG p a rsin g algorithm and,

moreover, has the capability of controlling parsing strategies to avoid exhaustive searches. The pa rse r is extended to tre a t constraints in Typed F e a tu re S tru ctures (TFS) by using TFSP links (as defined in Section 3).

The analysis method proposed in this paper is used in the analysis module of the NADINE system[4-9i and the NADINE system is used as the machine tra n sla tio n module of the SL-TRANS system. In the SL-TRANS system, input speech is recognized by the Jap an e se bun setsu1 phrase recognition module based on the HMM-LR method!8! and the module outputs the sequence of bunsetsu phrase lattices, each of which consists of bunsetsu phrase stru c tu re candidates. The outputs are filtered by a bunsetsu dependency filter module(51 which outputs sentence lattices consisting of fewer bunsetsu phrase stru c tu re candidates than the HMM-LR produces.

The NADINE system takes as its input a sentence lattice and outputs an English sentence. The analysis module based on this p aper’s method takes a sentence lattice and outputs typed f e a tu re s t r u c t u r e s which r e p r e s e n t sy n ta c tic , s e m a n tic and p r a g m a tic in fo rm a tio n of the sentence. Then, the tra n sfe r and generation modules output an English sentence.

In this paper, Section 2 describes the g ra m m a r fram ework and Section 3 describes the parser and the analysis method.

2. G R A M M A R F R A M E W O R K F O R S P O K E N - S T Y L E J A P A N E S E S E N T E N C E S

The g r a m m a r built up to analyze spoken-style Ja p a n e s e sentences is essentially based on HPSG and JPSG . The g ra m m a r describes not only syntactic and sem antic information but also discourse and pragm atic information in an in tegrated way by using TFS descriptions.

Resolution of om itted obligatory cases (or zero-pronouns) is very im p o rta n t because

Fig. 1 Overview of the SL-TRANS system (modules related to the a n a ly sis module)

1. a basic phonological ph rase consisting of a jiritsugo-word such as a noun, verb, or adverb followed by zero or more fuzokugo-v/ords such as au xiliary verbs, postpositional p a rtic le s, or sentence final particles.

(i) pronouns referring to the speaker and the h earer seldom appear in spoken-style sentences and these om itted cases make sentences more ambiguous, and

(ii) in o rd er to t r a n s l a t e these se n te n c es into n a tu r a l E n g l is h s e n te n c e s , they m u s t be supplemented.

If they are not supplem ented, for ex am p le, J a p a n e s e s e n te n c es w ith o u t a g e n t subject case e xp re ssion s m u s t often b e . t r a n s l a t e d into u n n a t u r a l E n g lish passive s e n te n c e s (e.g., “A registration form will be sent” instead of “I will send you a registration form '). In this paper's analysis, such omitted cases are resolved by using constraints on the uses of deictic expressions

and their case elements, and so on.

2.1. Treatm ent of Syntactic and Sem antic Information

Spoken-style J a p a n e s e s e n te n c es often have complex se n te n c e final predicate p h ra s e s consisting of m ain predicates and combinations of auxiliary verbs and sentence final particles. In

such a predicate p h rase , its head c o n s ti tu e n t s ti p u l a t e s the p ro p e rtie s of the c o m plem ent occurring ju s t on its left such as its p a rt of speech, conjugational type, and conjugational form. Such stipulations are easily described in the SUBCAT feature value in the head. A SUBCAT

feature value is a list of complement constituent specifications.

For example, in the lexical description (1) of the causative au xiliary verb “seru”, the SUBCAT f e a tu re v alu e specifies t h a t the a u x il ia r y t a k e s as its c o m p l e m e n t a v e rb p h r a s e w ith conjugational type CONS (for consonant type) and conjugational form VONG (for voice negative type), and two postpositional phrases (PPs), a PP m arked by “ni” and a PP m arked by ga . Moreover, it specifies t h a t the VP m ust be located ju s t before the auxiliary and t h a t the relative order between two PPs is free. The SEMF feature, which is a bundle of s e m a n tic fe a tu re s ,

specifies the sem antic selectional restrictions and, in the description, the SEMF feature value of

the ga-PP specifies t h a t the PP m ust refer to an a nim ate object.

[[syn [[morph [[ctype vow][cform aspl-or-infn]]] [head [[pos v]

[modi [[caus +]]]

[subcat [[first [[syn [[morph [[ctype cons][cform vong]]] [head [[pos v]

[modi [[caus -][deac -] ...]]]] [subcat [[first [[syn [[head [[form ga]

. . . ] ] . . . ] . . . ] [sem ?causee] ] ] [rest end ] ] ] ] ]

[sem ?caused]]]

[rest ( :perm-list [[syn [[head [[formga] ...]] ...]] [semf [[human +]]]

[sem ?causer]]

[[syn [[head [[formni] ...]] ...]] [sem ?causee]])]]] ...] ...]

[sem [[relation cause] [causer ?causer] [causee ?causee]

[ c a u s e d ? c a u s e d ] ] ] ] ^

where “?” is the prefix of the tag and s tru c tu re s denoted by the sam e tag are token identical, and ":perm -list” is a macro which takes as its a rg u m e n ts a set of typed feature s tr u c tu r e descriptions

F u r t h e r m o r e , t h e C O H f e a t u r e ( C a t e g o r y O f H e a d ) in a c o m p l e m e n t o r a d j u n c t c o n s t i t u e n t s p e c i f i e s i t s h e a d c o n s t i t u e n t s . C o m b i n a t i o n s o f C O H a n d S U B C A T f e a t u r e s a l l o w f l e x i b l e g r a m m a t i c a l d e s c r i p t i o n s .

J a p a n e s e p r e d i c a t e c o n s t i t u e n t s b e l o n g to g r o u p s : a m e m b e r o f t h e s e g r o u p s m u s t , w i t h s o m e e x c e p t i o n s , o c c u r in a s t r i c t l y o n e - d i m e n s i o n a l s e q u e n c e ; t h e s e g r o u p s c o r r e s p o n d to s e m a n t i c h i e r a r c h i e s . A n e w h e a d f e a t u r e M O D L (fo r m o d a l i t y ) h a s b e e n d e v i s e d to a l l a n d o n l y p r e d i c a t e s w i t h g r a m m a t i c a l l y o r d e r e d c o n s t i t u e n t s . F o r e x a m p l e , in t h e a b o v e d e s c r i p t i o n ( 1 ) , t h e M O D L f e a t u r e v a l u e o f t h e f i r s t S U B C A T v a l u e e l e m e n t s p e c i f i e s t h a t t h e c o m p l e m e n t v e r b p h r a s e s h o u l d n o t i n c l u d e a n y a u x i l i a r y v e r b s .

B e s i d e s t h e p r e d i c a t e c o n s t i t u e n t o r d e r s p e c i f i c a t i o n , t h e M O D L f e a t u r e i s a l s o u s e d to r e s t r i c t s y n t a c t i c a n d s e m a n t i c b e h a v i o r o f s u b o r d i n a t e ( a d v e r b i a l ) p h r a s e s . F o r e x a m p l e , c e r t a i n f o r m a l a d v e r b s ( i . e . , s u b o r d i n a t e c o n j u n c t i o n s ) r e q u i r e a s t h e i r c o m p l e m e n t s v e r b p h r a s e s w i t h o u t t i m e o r p l a c e m o d i f i e r s . S u c h r e q u i r e m e n t s r e d u c e a m b i g u i t i e s o f a d v e r b i a l p h r a s e m o d i f i c a n d s . T h e M O D L f e a t u r e in c o n j u n c t i o n w i t h t h e S E M F f e a t u r e c o n t r i b u t e to r e d u c i n g t h e n u m b e r o f v e r b a l m o d i f i c a n d a m b i g u i t i e s .

2 . 2 . T r e a t m e n t o f P r a g m a t i c C o n s t r a i n t s o n U ses of E x p r e s s i o n s

This g r a m m a r fram ew ork tre a ts discourse or p ragm atic co n straints on uses of expressions in

order to select plausible analysis candidates and to resolve certain kinds of zero-pronouns. An analysis c a n d id a te includes not only s y n ta c tic o -s e m a n tic d e s crip tio n s such as a s e m a n tic in te rp re ta tio n (the SEM feature value) bu t also annotation s or a set of conditions under which the in te rp re ta tio n is valid. For example, the sentence

Watashi ni tourokuyoushi o o-okuri itadake masu ka I DAT registration form ACC HON-send RECEIVE-FAVOR POLITE QUESTION

seems to have two a nalysis candidates corresponding to phrase stru c tu re s (a) and (b) in Fig.2 (they correspond to “Could you please send me a registration form ?” and IT 'C ould I please send a registration form ?”). However, the analysis candidate corresponding to (b) has the following annotations:

common p h rase s tr u c tu r e of (a) and (b)

phrase s tr u c tu r e (a) v

---ph rase s tr u c tu r e (b)

W a ta s h i ni tourokuyoushi wo o-okuri itadake m asu

Fig.2 Two de rivation trees of the sentence ‘w a ta sh i ni tourokuyoushi o o-okuri itadake m asu k a “

[ [ r e l a t i o n c o n d e s c e n d ] [ a g e n t ? s p e a k e r ] [ o b j e c t ? s u b j e c t _ s e m ] [ c o m p a r a t i v e - o b j e c t ? s p e a k e r ] ]

[ [ r e l a t i o n e x p r e s s - m o r e - e m p a t h y ] [ a g e n t ? s p e a k e r ]

[ o b j e c t ? s u b j e c t _ s e m ] [ c o m p a r a t i v e - o b j e c t ? s p e a k e r ] ]

( where ? s p e a k e r refers to the speaker and ? s u b j e c t _ s e m is the semantic representation of the subject of “itadake”).

Accordingly, these conditions are u n n a tu ra l (e.g., the speaker expresses more e m p a th y to a

person other th an himself) but (a) does not have such u n n a tu ra l conditions. Thus, the analysis (a) is selected as a more plausible candidate than (b).

These annotations are also used for zero-pronoun resolution. In the analysis (a), the subject and indirect object o {"'itadake' are missing. However, (a) has the following annotations:

[ [ r e l a t i o n c o n d e s c e n d ] [ a g e n t ? s p e a k e r ] [ o b j e c t ? s u b j e c t _ s e m ]

[ c o m p a r a t i v e - o b j e c t ? i n d i r e c t - o b j e c t _ s e m ] ] [ [ r e l a t i o n e x p r e s s - m o r e - e m p a t h y ]

[ a g e n t ? s p e a k e r ] [ o b j e c t ? s u b j e c t _ s e m ]

[ c o m p a r a t i v e - o b j e c t ? i n d i r e c t - o b j e c t _ s e m ] ]

and by searching for discourse participants satisfying these conditions, candidates of missing elem ents can be found.

In order to obtain such annotations, lexical descriptions have PRAG| RESTRS features which include co n strain ts in term s of RESPECT, CONDESCEND, POLITE, EXPRESS-M O RE- EMPATHY and so on.

P lausibility scores based on these annotations are used in conjunction with other kinds of

scores described below to select plausible analysis candidates. Zero-pronoun resolution is applied after p arsin g A nnotations are used in conjunction with conditions under which u tte ran c e s of sentences are in te rp re te d as c e rtain types of illocutionary acts, and conditio ns u n d e r which

actions in general a re rational.

3. FEATURE STRUCTURE PROPAGATION PARSER

3.1. A ctive Chart Parser w ith Feature Structure Propagation Links

The active c h a rt pa rsin g algo rithm has properties suitable for pa rsing n a t u r a l la n g u a g e

efficiently. In p a rtic u la r, it has two excellent properties for tre a tin g speech recognition resu lt

lattices:

(i) it does not lim it its inputs to only strin g s but can accept lattice stru c tu re s — thus, it can parse

speech recognition re s u lt lattices directly; and,

However, this second property makes stru c tu re sharing difficult in unification-based CFG parsing, or CFG parsing augm ented by con straints described in typed feature s tru c tu re s (TFSs). In unification-based parsing, there often exist edges with the same content except for their TFSs. When an active edge is continued with an inactive edge, if there is already an edge with the same contents except for its TFSs as the continuation edge, edge s h a rin g may seem to be able to be achieved by adding the continuation edge's TFSs into the existing edge’s. However, this makes parsing incomplete because the existing edge may have been used previously to construct larger

edges due to the parsing order freeness and because newly added TFSs are not used to construct larger edges or used as p a rt of larger edges.

In order to solve this problem, the T F S P r o p a g a t i o n p a r s e r (in short, T F S P p a r s e r ) has been developed. The parser is essentially based on active c h a rt parsing and each edge of the

p a r s e r h a s a set of TFS s r e p r e s e n t in g sy n ta c tic , s e m a n tic and p r a g m a tic in fo r m a tio n of corresponding p artial phrase structures. The parser is extended to have special links called T F S

P ropagation links (TFSP links).

A T FSP link in an edge rem em bers how the TFSs of the edge were previously propagated and specifies how TFSs newly added into the edge should be used. T h a t is, a TFSP link of an active edge points to a continuation edge having as its annotation the inactive edge used to construct the

continuation edge. Then, when a TFS is added to an active edge, for each TFSP link of the edge, the TFS is unified with each TFS of the link's inactive edge and then the unification result TFS is added into the link's continuation edge if the unification succeeds. By using T FSP links, new edge creation is necessary only when there is no edge with a ce rtain s ta rtin g vertex, ending vertex, label and rem a in d e r symbol sequence. The T FSP link m akes edge s tru c tu re sh a rin g possible.

Fig.3 T FS P links

Suppose the case where the inactive edge G has been created from the active edge (D and the inactive edge G and the inactive edge © has been created from the active edge ® and the inactive edge G . The T F S P link © is created between G and © . In this case, when the active edge ® is continued with the inactive edge G , the successful unification re s u lt TFSs of ® ’s and G ’s TFSs a re added to the edge G . The edge has a T FS P link and then the newly added TFSs are unified with T FSs in ® and the successful unification re su lts are propagated to the edge © as specified by the T FS link G . If there are already TFS links in the edge © , the newly added T FS s a re also p ropagated in the ways specified by these links.

The TFSP link enables the parser to reduce unnecessary edge stru cture creation and TFS unification. When an active edge is continued with an inactive edge, the continuation edge is meaningful only when it has at least one consistent TFS corresponding to the continuation edge. Therefore, the necessary computation is reduced to finding a pair of active and inactive edge TFSs which are consistent or can be unified. It is not necessary to compute the other pairs' unification after finding a first pair unless TFSs representing whole sentence structures are required later. This is made possible by using TFSP links because they can not only unify TFSs immediately and propagate unification result if desired, but they can also propagate information on how to unify them later. This reduces unnecessary unification computation when the edges are not used as parts of the parses of the whole sentences, especially when the TFSP parser does not need to find all possible parses exhaustively.

The unification method used in the T FSP parser has the following characteristics:

(1) It uses Kasper's disjunctive feature s tru c tu re unification algorithm ^). This allows not only for efficient descriptions of each lexical item (such as efficient coding of SUBCAT feature values for tre a tin g complement order scram bling and word m eanings with conditions for disambiguation), but also packing descriptions of homonyms. Disjunctive lexical descriptions work like Polaroid wordsl31.

(2) As for the definite feature s tru c tu re unification algorithm , the increm ental copy unification a lgorithm which allows cyclic s tr u c tu r e s ! 7! is ad opted to t r e a t cyclic c o n s tr a in t s includ ing

SUBCAT and COH features.

3.2. A genda Control M echanism and P lausib ility Score

In order to select the most plausible analysis candidate in the early stages, the TFSP p arser selects the pending edge with the best edge score among the pending edge list du rin g parsing, and selects the TFS with the best TFS score among sets of TFSs in complete edges, each of which has as its label the s ta r t symbol, as its rem a in d e r symbol sequence an empty sequence, as its s ta rtin g vertex the leftmost vertex of the chart, and as its ending vertex the rightm ost vertex of the c h a rt

j u s t after parsing finishes. P a rsin g finishes when a certain num ber of TFSs have been created with scores better th an certain c riteria determ ined by the input sentence length (e.g., the num ber

of bunsetsu structures).

The edge score m ainly contributes to first obtaining a plausible syntactic structure. The edge score for tre a tin g speech recognition r e su lt lattices is essentially based on the following:

(a) speech recognition score, (b) surface strin g length, and

(c) edge type such as active, inactive, or just-proposed.

W hen a new edge is created, the edge score is calculated from information on the active edge and the inactive edge. Moreover, when a new TFSP link is created and the links point to an existing

continuation edge, the edge score of the continuation is recalculated.

The TFS score m ainly contributes to obtaining syntactico-sem antically and p ragm atically

plausible s tru c tu re and is essentially based on the following:

(d) ph rase s tr u c tu r e complexity (the nu m ber of phrase s tru c tu re tree nodes), (e) unfilled com plem ents (the n um ber of elem ents in SLASH feature value), and

(0 violation of prag m a tic c o n stra in ts on expression usage (the u n n a tu r a l relationships in the

PRAG|RESTRS feature value).

The behavior of the T F S P p a rse r is illu strated by an example. Suppose the case where a speech recognition re su lt lattice includes the following sentence candidates and the nom inative

postposition “g a” has a better speech recognition score than the topic m ark er "wa'” (Fig.4). The parser first tries to build up the stru c tu re including "ga” due to the speech recognition score preference because there are no other differences between stru c tu re s including “g a ” and "wa”. H o w e v e r, th e b u i ld i n g - u p p ro ce ss sto p s w hen c o m b in in g s t r u c t u r e s c o r r e s p o n d i n g to “tour okay oils hi ga ” and “o-okuri” because of TFS unification failu re betw een SEM F fea tu re values of the verb's subject [[animate + 1] and the nominative noun phrase [[animate -]]. Then, the p arser adopts the s tru c tu re containing “wa” and analyzes the sem antics of the topic noun phrase as playing a sem antic object role in the “o k u r u ” (sending) relationship.

In th is case, the a g e n t s u b je c t is m is s in g a n d th e p a r s e r o u t p u t s as the s e m a n t i c representation:

[[relation okuru-1] [agent ?subject_sem]

[recipient ?indirect-object_sem] [object [[parameter ?x]

[restriction [[relation tourokuyoushi-1] [object ?x] ] ] ] ] ]

However, the p arser also outputs pragm atic con strain ts on the person referred to by the subject based on the lexical descriptions of the honorific verb “itashi” as follows:

[[relation condescend]

[agent ?speaker]

[object ?subject_sem]

[comparative-object ?indirect-object_sem]]

After parsing, the analysis module searches for the person to whom the speaker can condescend, and if there is no person other th an the speaker and the h e a re r in the discourse of the utterance,

the m issing subject is analyzed as refe rrin g to the s p e a k e r. T h e n , the following s e m a n tic re p re sen ta tio n is obtained:

[[relation okuru-1] [agent ?speaker] [recipient ?hearer]

[object [[parameter ?x]

[restriction [[relation tourokuyoushi-1] [object ? x ] ] ]] ] ]

From this sem an tic rep re sen ta tio n , the o u tp ut sentence “I send you a r e g i s tr a ti o n fo rm .” is obtained.

(Lit.) A re g is tra tio n fo rm w i l l send (som ething).

a

Tourokuyoushi N O M \ o-okuri itashi masu -*-•---»♦--- x>

R e gistration fo rm HON-send do-CONDECEND POLITE

UTPfC

(Lit.) As f o r the re g is tra tio n fo rm , (I) w i l l send it. ° Bunsetsu b o u n d a ry

Fig.4 Exam ple of speech recognition re s u lt lattice sequence (simplified).

This analysis method is applied to speech recognition results of sentences in 2 task-oriented dialogues about “the secretarial service of the international conference”. The HMM-LR speech recognition module with a bu nsetsu dependency filter o u tp u ts for each spoken sen ten ce a sequence of bunsetsu phrase lattices. These 2 dialogues consist of 37 sentences. The speech recognition module outputs correct results (i.e., sequences of bunsetsu lattices each of which includes the correct bunsetsu structure) for 35 sentences. This analysis method is applied to these 35 sentences.

These sentences consists of 76 bunsetsu phrases and 112 bunsetsu struc ture candidates. T h at is, a bunsetsu phrase has about 1.47 bunsetsu struc ture candidates.

For this experim ent, a g ra m m a r was prepared which includes not only lexical items required for accepting correct bunsetsu stru c tu re s in the dialogue, but also all lexical items consisting of all bunsetsu stru c tu re candidates. The g ra m m a r consists of 13 general rules including morphological rules and about 300 lexical entries.

The analysis method obtains correct sentence analysis results for 34 sentences; adequate English sentences are obtained from these correct analysis results. The sentence recognition rate of this method is about 97% and the total sentence recognition rate including the HMM-LR speech recognition module is 92%. The single incorrect analysis result structu re, which corresponds to the Ja p a n e s e sentence “tourokuyoushi mo o-okuri itashi masu ‘ (lit. “I will send you a registration form, too") instead of “tourokuyoushi o o-okuri itashi m a su“ (lit. ‘7 will send you a registration form"), includes as the incorrect speech recognition pa rt only an incorrect modal particle “m o” with a h igher speech recognition score than the correct case particle “o”, and the incorrectly recognized stru c tu re is perfectly gram m atical. In this case, to obtain the correct result requires taking account of the differences in presuppositions derived from these particles and comparing these presuppositions with the context of the utterances.

4. CONCLUSION

In this paper, a new analysis method is proposed for J a p a n e s e spoken sentences u sin g a g r a m m a r fram ew ork for tre a tin g spoken-style Ja p a n e s e sentences and a new p a rse r called the T FSP parser. The g r a m m a r fram ew ork is essentially based on HPSG and JPSG , and is designed

to tre a t not only syntactic and sem antic information but also p ragm atic information. Analysis results based on this f ra m e w o rk include s e m a n tic i n t e r p r e t a t i o n s of in p u t se n te n c e s with anno tatio ns on c o n stra in ts on the uses of these sentences. The T FSP parser has been developed to allow edge s tru c tu re s h a rin g in unification-based analyses. This method is used as the analysis module of the NADINE system and the SL-TRANS system.

The analysis method is applied to HMM-LR speech recognition result lattices. In parsing

lattices, selecting the pending edge with the best score allows the pa rse r to first find plausible candidates. C o n stra in ts described in TFSs filter out syntactically or sem antically ill-form ed structu res. The ex perim en tal resu lts show th a t this method is effective in s e n te n c e speech recognition. In the experim ents, recovering from incorrect recognition requires utte ranc e context u n d e rs ta n d in g including un d e rs ta n d in g of utterance presuppositions.

ACKNOWLEDGEMENTS

The a u th o r is deeply grateful to Dr. K u re m a tsu , the president of ATR In te rp re tin g Telephony Research Laboratories, T e ru a k i Aizawa, the head of the N a t u r a l L a n g u a g e U n d e r s ta n d i n g

3.3. Ex p er ime nt s

D e p a r tm e n t, Kei Yoshimoto, and the m em b e rs of the N a t u r a l L an gu age U n d e r s t a n d i n g D epartm en t for their constant help and encouragement.

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