公司 標誌 Question Answering System Introduction to Q-A System 資訊四 B 張弘霖 資訊四 B 王惟正

Reference  “Open-Domain Voice-Activated Question Answering” - COLING’02  "Study on spoken interactive open domain question answering" - SSPR’03  “Language models and dialogue strategy for a voice QA system “ - ICA’04

Open –Domain Voice- Activated Question Answering  Date: 2006/5/22  Author: Sanda Harabagiu, Dan Moldvan, Joe Picone.2002  Abbreviation: VAQA

Outline 1. Instruction and Motivation 3. Experiment result and conclusion 2. Four major components VAQA

Instruction and Motivation  Open-Domain Question Answering (ODQA) is popular, especially on Internet domain because of its rich source.  Text-based: yahoo, google  Why voice-activated QA is need?  Mobile device, keyboard bottleneck  Voice input is fast and convenient VAQA

Instruction and Motivation  Basic component for VAQA  Automatic Speech Recognition(ASR)  Q&A system  Simple path: ASR  Q&A (not good, latter)  Our solution: ASR  Q&A VAQA

Instruction and Motivation VAQA Global view for Voice- Activated Question Answering System On-line Documents

Instruction and Motivation  Filtering: ill-formed questions from the word lattice.  Alternation of keywords.  Interactive Q&A module.  Enhanced language model. VAQA

Instruction and Motivation  Simple path (ASR  Q&A)  TREC8 and 9  ISSP with 30% WER  76% ↓ 7%  Iterative refinement! Interaction between ASR and Q&A make better performance than individual components. VAQA

Four components  Alternation (Harabagiu et al. 2001)  Three keyword variants.  Morphological invent, invention, inventor  Semantic murderer and killer  Lexical paraphrase Like better and prefer VAQA

Four major components  Filtering Goal: significantly reduce the large number of outputs produced by the word lattice search module. VAQA

Four major components 1.Syntactic filter: “The was President Cleveland wife” “When President Cleveland life” 2.Semantic filer: “It was President Cleveland lawyer” for “Who is President Cleveland wife”; “Who is President Cleveland’s life” VAQA

Four major components 3.Pragmatic filter: ”How far is Yaroslavl from Moscow?” Even if the city name is not recognized, one of question pattern set will identify the first concept after the question stem is “location.” VAQA

Four major components VAQA Architecture of filtering component in VAQA

Four major components VAQA Global view for Voice- Activated Question Answering System On-line Documents

Four major components  Enhanced Language Model  Language Model  A mechanism to estimate the probability of some word w in q word sequence W given the surrounding words. [prob.”I am” > prob.”I aim” ]  Linguistic, domain, pragmatic knowledge.  N-gram model for most ASR, local dependencies between words. VAQA

Four major components  Enhanced Language Model  N-gram is insufficient for the recognition of spoken question words.  “How far is Yaroslavl from Moscow”  “Affair is yes level from Moscow”  Probability for Affair and Moscow VAQA

Four major components  Enhanced Language Model  Semantic transformation of questions (Harabagiu et al. 2000)  Graphs in witch the edges are binary dependencies and question stems are replaced by semantic classes e.g. PERSON, DISTANCE. VAQA How far is Yaroslavl from Moscow

Four major components  Semantic Transformation of Questions  “Affair is yes level from Moscow”  “from Moscow” + ST + Stem  correction of “affair” to “how far”. VAQA

Four major components  Enhanced Language Model  Recognized Question  Semantic transformation S  The Question Q  A set of binary dependency  The base NPs recognized by the parser  Question semantic template information VAQA

Four major components  Question Template  Semantic Class v.s. Expected Answer Type VAQA

Four major component  Recognized Question  Semantic transformation S  The question Q VAQA

Four major components VAQA Global view for Voice- Activated Question Answering System On-line Documents

Four major components  Interactive Question Answering  Due to the errors from ASR.  Clarification from user by asking question.  Steps:  Finding the conflicts in the question recognized.  Deciding what the question is about.  Re-solve the question with the feedback.  Rank the keywords for answering. VAQA

Four major components  Interactive Question Answering  Example:  “Where” identifies expected answer type as LOCATION.  “leader” is a member the PERSON subhierarchy.  “leader” is the focus of the question. VAQA

Four Major components  Interactive Question Answering  “No” indicates the system did not comprehend the topic of the question.  “musical” and “summer” are new keywords. VAQA

Four major components  Interactive Question Answering  “Where”  at least one location  “musical”  “summer”, dropped because the number of paragraphs is too small. VAQA

Four major components VAQA Global view for Voice- Activated Question Answering System On-line Documents

Experiment result and conclusion  Experiment result  RAR (reciprocal value of the rank)  RAR = 1 / rank  MRAR = 1/n ΣRAR VAQA

Experiment result and conclusion  Experiment result  Word Error Rate (WER) for ASR VAQA

Experiment Result and conclusion  Conclusion  Performance of VAQA depends mostly on ELM and correction in IQA module.  To train the ELM, filtering component is essential. why?  Experiment result reveals VAQA here both improves:  Accuracy of spoken Q&A  Better WER of ASR VAQA

Global view for Voice- Activated Question Answering System On-line Documents

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