Leveraging Relevance Cues for Improved Spoken Document Retrieval

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1 Leveraging Relevance Cues for Iproved Spoken Docuent Retrieval Pei-Ning Chen 1, Kuan-Yu Chen 2 and Berlin Chen 1 National Taiwan Noral University, Taiwan 1 Institute of Inforation Science, Acadeia Sinica, Taiwan 2 Interspeech 2011

2 Outline Introduction Language Modeling for Inforation Retrieval (IR) Various Relevance Models Proposed in this paper Experiental Results Conclusions and Future Work 2

3 Introduction Large volues of ultiedia associated with speech are now ade available on the Internet Speech retrieval provides a natural way for ultiedia access It has been extensively studied and practiced in the speech processing counity Spoken Docuent Processing & Speech Recognition Inforation Need 3

4 Introduction (cont.) Task Definition for speech retrieval Robustly Index spoken docuents with speech recognition techniques Retrieve relevant spoken docuents in response to a user query Spoken Ter Detection (STD) Find literally atched spoken docuents where all/ost query ters should be present (uch like Web search) Spoken Docuent Retrieval (SDR) Find spoken docuents that are topically related to a given query 4

5 Introduction (cont.) The fundaental probles facing SDR are generally three-fold First, a query is often only a vague expression of an underlying inforation need Word usage isatch between a query and a spoken docuent even if they are topically related to each other The iperfect speech recognition transcript carries wrong inforation and thus deviates soewhat fro representing the true thee of a spoken docuent 5

Language Modeling for IR LM approaches have been introduced to IR (and SDR), and deonstrated with good success P LM ( D ) = ( ) ( D) P D P ( ) P P( D) The Kullback-Leibler (KL)-Divergence easure is

6 Language Modeling for IR LM approaches have been introduced to IR (and SDR), and deonstrated with good success P LM ( D ) = ( ) ( D) P D P ( ) P P( D) The Kullback-Leibler (KL)-Divergence easure is another basic forulation of LM for IR ( w ) ( w D) P KL( D) = P( w ) log P P w V w V ( w ) logp( w D) A query is treated as a probabilistic odel rather than siply an observation KL-divergence supports us to iprove not only the docuent odel but also the query odel for better docuent ranking 6

7 Relevance Modeling (RM) In the conventional relevance odeling Each query is assued to be associated with an unknown relevance class R Docuents that are relevant to the inforation need expressed in the query are saples drawn fro The docuent ranking proble can be reduced to deterine the probability P RM ( w ) The relevance odel can be defined as the probability of the word selected fro relevance docuents P RM ( w ) M = 1P( D ) P( q1,, ql, w D ) M P( D ) P( w D ) L P( q D ) = = 1 l= 1 l R uery R Ranked Docuents D 7

8 Incorporating Topical Inforation in RM Topic-based relevance odel (TRM) akes a step forward by incorporating latent topic inforation into RM As conventional topic odels, the probability that a word occurs is estiated fro a set of latent topics ( w ) K P( D ) P( T D ) P( w T ) L P( q T ) PTRM M = 1 k = 1 k k l = 1 l k TRM has soe assuptions and properties: Relevant docuents are assued to share a set of pre-defined latent topic variables { T 1,,T K } When given a latent topic, words and docuents are independent of each other TRM assues that the additional cues of how words are distributed across a set of latent topics can carry useful global topic structure for relevance odeling 8

9 Modeling Pairwise Word Association in RM RM and TRM be used to odel the association between an entire query and a word w We propose Pairwise-based RM (PRM) to render the pairwise word association between a word in the query and any word It can be regarded as a kind of LM for translating words in the query to w P w q M P D P w D P q D PRM ( ) ( ) ( ) ( ) l = 1 1 P ( ) = L PRM w l = 1PPRM ( ql,w ) L Again, a set of latent topics is introduced into PRM (denoted by TPRM) to describe the word-word co-occurrence relationships ( q,w ) K P( D ) P( T D ) P( q T ) P( w T ) PTPRM l M = 1 k = 1 k l l k q l k 9

10 Inference of the Relevance Class In practice, the relevant docuents are unknown in advance First-round retrieval with the query-likelihood LM approach is applied to obtain a set of top-ranked (pseudo-relevant) docuents to approxiate the relevance class Second-run retrieval with the KL-divergence easure is used to re-rank the spoken docuents uery Retrieval Results First-run Retrieval Spoken Docuent Collection Second-run Retrieval Top-Ranked Docuents Relevance Class Relevance Model 10

11 Different Granularities of Index Features Word-level index features possess ore seantic inforation than subword-level ones Enhance the precision Subword-level index features are ore robust against the open vocabulary proble and speech recognition errors Enhance the recall Distinct syllable pairs occurring in the spoken docuent collection were then identified to for a vocabulary of syllable pairs for indexing 11

12 Incorporating Non-Relevance Inforation Further, in addition to using the relevance inforation, we also hypothesize that the non-relevant (low-ranked) docuents can provide extra useful cues For this idea to work, we attept to estiate a non-relevance odel P w NR for each test query The non-relevance odel can be estiated siply based on the ML criterion or be further optiized with the EM algorith E-step: M-step: ( ) P P ( NR w) ( w NR ) = = λ P w D' D λ P( w NR ) ( w NR ) + ( 1 λ) P( w BG) D' D Low c Low ( w,d' ) P( NR ) w c( w,d' ) P( NR w) 16

Inforation Note also that Here we adopt an unsupervised way to estiate the nonrelevance odel We intend to

13 Incorporating Non-Relevance Inforation The siilarity easure between query D thus can be coputed as follows: and any docuent (,D) = SIM (,D) + α KL( NR D) SIM RM Relevance Inforation Penalty Factor Non-Relevance Inforation Note also that Here we adopt an unsupervised way to estiate the nonrelevance odel We intend to explore whether the relevance and non-relevance cues of a test query can conspire to enhance the SDR perforance 17

14 Conclusions In this paper, we have investigated a relevance language odeling fraework for SDR The utility of the ethods have also been validated by extensively coparisons with several widely used retrieval ethods The experiental results indeed deonstrate the applicability of our ethods As to future work, we envisage two directions: One is utilizing speech suarization techniques to help better estiate the query and docuent odels The other is training the query and docuent odels in a lightly-supervised anner through the exploration of users click-through data 19

15 Thank you! uestions? 20

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