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UID:03e906d83e472a857fc2281cf32d0733-194
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DESCRIPTION:A Syntactic Neural Model for Question Decomposition
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/194/a-syntactic-neural-model-for-question-decomposition/
SUMMARY:Question decomposition along with single-hop Question Answering (QA) system serve as useful modules in developing multi-hop Question Answering systems, mainly because the resulting QA system is interpretable and has been demonstrated to exhibit better performance. The problem of Question Decomposition can be posed as a machine translation problem and it can be solved using any sequence-to-sequence neural architecture. Using this approach, it is difficult to capture the innate hierarchical structure of the decomposition. Inspired by database query languages a pseudo-formalism for capturing the meaning of questions, called Question Decomposition Meaning Representation (QDMR) was recently introduced. In this approach, a complex question is decomposed into simple queries which are mapped into a small set of formal operations. This method does not utilize the underlying syntax information of QDMR to generate the decomposition.
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In the area of programming language code generation, methods that use syntax information as a prior knowledge have been demonstrated to perform better. Moreover, the syntax-aware models are usually interpretable.
Motivated by the success of syntax-aware models, we propose a new syntactic neural model for question decomposition in this thesis.
In particular, we encode the underlying syntax of the QDMR structures into a grammar model as a sequence of actions.
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This is done using a deterministic framework which uses Abstract Syntax Trees (AST) and Parse Trees. The proposed
approach can be thought of as an encoder-decoder method for QDMR structures where a sequence of possible actions is a latent representation of the QDMR structure. The advantage of using this latent representation is that it is interpretable. Experimental results on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art approach especially in scenarios where training data is limited. Some heuristics to further improve the performance of the proposed approach are also suggested in this work.
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Microsoft teams link:<br>
<a href="https://teams.microsoft.com/l/meetup-join/19%3ameeting_YTQ3YzdhOWMtMDBiYy00ODQxLWJmMDItMzlmY2MwNjFjYjY5%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%229c3e2bfe-1b0f-4d7b-a589-832878069dc6%22%7d">https://teams.microsoft.com/l/meetup-join/19%3ameeting_YTQ3YzdhOWMtMDBiYy00ODQxLWJmMDItMzlmY2MwNjFjYjY5%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%229c3e2bfe-1b0f-4d7b-a589-832878069dc6%22%7d</a>
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