A Real World Implementation of Answer Extraction

In this paper we describe ExtrAns, an answer extraction system. Answer extraction (AE) aims at retrieving those exact passages of a document that directly answer a given user question. AE is more ambitious than information retrieval and information extraction in that the retrieval results are phrases, not entire documents, and in that the queries may be arbitrarily specific. It is less ambitious than full-fledged question answering in that the answers are not generated from a knowledge base but looked up in the text of documents. The current version of ExtrAns is able to parse unedited Unix "man pages", and derive the logical form of their sentences. User queries are also translated into logical forms. A theorem prover then retrieves the relevant phrases, which are presented through selective highlighting in their context.Comment: 5 page

On the Scalability of the Answer Extraction System "ExtrAns"

This paper reports on the scalability of the answer extraction system ExtrAns. An answer extraction system locates the exact phrases in the documents that contain the explicit answers to the user queries. Answer extraction systems are therefore more convenient than document retrieval systems in situations where the user wants to find specific information in limited time. ExtrAns performs answer extraction over UNIX manpages. It has been constructed by combining available linguistic resources and implementing only a few modules from scratch. A resolution procedure between the minimal logical form of the user query and the minimal logical forms of the manpage sentences finds the answers to the queries. These answers are displayed to the user, together with pointers to the respective manpages, and the exact phrases that contribute to the answer are highlighted. This paper shows that the increase in response times is not a big issue when scaling the system up from 30 to 500 documents, and that the response times for 500 documents are still acceptable for a real-time answer extraction system.Comment: 5 page

Learning to answer questions

We present an open-domain Question-Answering system that learns to answer questions based on successful past interactions. We follow a pattern-based approach to Answer-Extraction, where (lexico-syntactic) patterns that relate a question to its answer are automatically learned and used to answer future questions. Results show that our approach contributes to the system's best performance when it is conjugated with typical Answer-Extraction strategies. Moreover, it allows the system to learn with the answered questions and to rectify wrong or unsolved past questions.Comment: 5 page

Semi-Supervised Few-Shot Learning for Dual Question-Answer Extraction

This paper addresses the problem of key phrase extraction from sentences. Existing state-of-the-art supervised methods require large amounts of annotated data to achieve good performance and generalization. Collecting labeled data is, however, often expensive. In this paper, we redefine the problem as question-answer extraction, and present SAMIE: Self-Asking Model for Information Ixtraction, a semi-supervised model which dually learns to ask and to answer questions by itself. Briefly, given a sentence $s$ and an answer $a$, the model needs to choose the most appropriate question $\hat q$; meanwhile, for the given sentence $s$ and same question $\hat q$ selected in the previous step, the model will predict an answer $\hat a$. The model can support few-shot learning with very limited supervision. It can also be used to perform clustering analysis when no supervision is provided. Experimental results show that the proposed method outperforms typical supervised methods especially when given little labeled data.Comment: 7 pages, 5 figures, submission to IJCAI1

S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension

In this paper, we present a novel approach to machine reading comprehension for the MS-MARCO dataset. Unlike the SQuAD dataset that aims to answer a question with exact text spans in a passage, the MS-MARCO dataset defines the task as answering a question from multiple passages and the words in the answer are not necessary in the passages. We therefore develop an extraction-then-synthesis framework to synthesize answers from extraction results. Specifically, the answer extraction model is first employed to predict the most important sub-spans from the passage as evidence, and the answer synthesis model takes the evidence as additional features along with the question and passage to further elaborate the final answers. We build the answer extraction model with state-of-the-art neural networks for single passage reading comprehension, and propose an additional task of passage ranking to help answer extraction in multiple passages. The answer synthesis model is based on the sequence-to-sequence neural networks with extracted evidences as features. Experiments show that our extraction-then-synthesis method outperforms state-of-the-art methods.Comment: AAAI1

Answer Extraction in Question Answering using Structure Features and Dependency Principles

Question Answering (QA) research is a significant and challenging task in Natural Language Processing. QA aims to extract an exact answer from a relevant text snippet or a document. The motivation behind QA research is the need of user who is using state-of-the-art search engines. The user expects an exact answer rather than a list of documents that probably contain the answer. In this paper, for a successful answer extraction from relevant documents several efficient features and relations are required to extract. The features include various lexical, syntactic, semantic and structural features. The proposed structural features are extracted from the dependency features of the question and supported document. Experimental results show that structural features improve the accuracy of answer extraction when combined with the basic features and designed using dependency principles. Proposed structural features use new design principles which extract the long-distance relations. This addition is a possible reason behind the improvement in overall answer extraction accuracy.Comment: 12 Pages, 11 Figures, 6 Tables, 4 Algorithms and IEEE Forma

Context-Aware Answer Extraction in Question Answering

Extractive QA models have shown very promising performance in predicting the correct answer to a question for a given passage. However, they sometimes result in predicting the correct answer text but in a context irrelevant to the given question. This discrepancy becomes especially important as the number of occurrences of the answer text in a passage increases. To resolve this issue, we propose \textbf{BLANC} (\textbf{BL}ock \textbf{A}ttentio\textbf{N} for \textbf{C}ontext prediction) based on two main ideas: context prediction as an auxiliary task in multi-task learning manner, and a block attention method that learns the context prediction task. With experiments on reading comprehension, we show that BLANC outperforms the state-of-the-art QA models, and the performance gap increases as the number of answer text occurrences increases. We also conduct an experiment of training the models using SQuAD and predicting the supporting facts on HotpotQA and show that BLANC outperforms all baseline models in this zero-shot setting.Comment: EMNLP 202