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A Syntactic Neural Model for Question Decomposition

Series: M.Tech (Research) Colloquium- ON-LINE

Speaker: Ms. Suman Gupta M.Tech (Research) student Dept. of CSA

Date/Time: Sep 03 14:00:00

Location: Microsoft Teams - ON-LINE

Faculty Advisor: Prof. Shirish K Shevade

Abstract:
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>
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