A Joint Model for Answer Sentence Ranking and Answer Extraction

Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i.e., as independent tasks. In this article, we (1) explain how both tasks are related at their core by a common quantity, and (2) propose a simple and intuitive joint probabilistic model that addresses both via joint computation but task-specific application of that quantity. In our experiments with two TREC datasets, our joint model substantially outperforms state-of-the-art systems in both tasks.

[1]  Lei Yu,et al.  Deep Learning for Answer Sentence Selection , 2014, ArXiv.

[2]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[3]  Jimmy J. Lin,et al.  Building a reusable test collection for question answering , 2006, J. Assoc. Inf. Sci. Technol..

[4]  Jennifer Chu-Carroll,et al.  Building Watson: An Overview of the DeepQA Project , 2010, AI Mag..

[5]  Adwait Ratnaparkhi,et al.  A Maximum Entropy Model for Part-Of-Speech Tagging , 1996, EMNLP.

[6]  Siddharth Patwardhan,et al.  Question analysis: How Watson reads a clue , 2012, IBM J. Res. Dev..

[7]  Noah A. Smith,et al.  What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA , 2007, EMNLP.

[8]  Chris Brockett,et al.  Aligning the RTE 2006 Corpus , 2007 .

[9]  Christopher D. Manning,et al.  Probabilistic Tree-Edit Models with Structured Latent Variables for Textual Entailment and Question Answering , 2010, COLING.

[10]  Claire Cardie,et al.  SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability , 2015, *SEMEVAL.

[11]  Chris Callison-Burch,et al.  PPDB: The Paraphrase Database , 2013, NAACL.

[12]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[13]  Noah A. Smith,et al.  Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions , 2010, NAACL.

[14]  Richard M. Schwartz,et al.  An Algorithm that Learns What's in a Name , 1999, Machine Learning.

[15]  Alessandro Moschitti,et al.  Automatic Feature Engineering for Answer Selection and Extraction , 2013, EMNLP.

[16]  Chris Callison-Burch,et al.  Answer Extraction as Sequence Tagging with Tree Edit Distance , 2013, NAACL.

[17]  Eneko Agirre,et al.  *SEM 2013 shared task: Semantic Textual Similarity , 2013, *SEMEVAL.

[18]  Eneko Agirre,et al.  SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity , 2012, *SEMEVAL.

[19]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[20]  Peter Clark,et al.  Automatic Coupling of Answer Extraction and Information Retrieval , 2013, ACL.

[21]  Steven Bethard,et al.  Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence , 2014, TACL.

[22]  Koby Crammer,et al.  Online Large-Margin Training of Dependency Parsers , 2005, ACL.

[23]  Alessandro Moschitti,et al.  Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks , 2015, SIGIR.

[24]  Steven Bethard,et al.  DLS@CU: Sentence Similarity from Word Alignment and Semantic Vector Composition , 2015, *SEMEVAL.

[25]  Georgiana Dinu,et al.  Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors , 2014, ACL.

[26]  Ming-Wei Chang,et al.  Question Answering Using Enhanced Lexical Semantic Models , 2013, ACL.

[27]  Chris Callison-Burch,et al.  Semi-Markov Phrase-Based Monolingual Alignment , 2013, EMNLP.

[28]  Claire Cardie,et al.  SemEval-2014 Task 10: Multilingual Semantic Textual Similarity , 2014, *SEMEVAL.