| Challenge: | Existing work on deep question-answering tasks from admission exams is challenging since it requires effective representation to capture complicated semantic relations between questions and answers. |
| Approach: | They propose a hybrid neural model for deep question-answering task from history examinations using a gated network and a machine labeler. |
| Outcome: | The proposed model obtains substantial performance gains over baseline models in terms of multiple evaluation metrics. |
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| Challenge: | Existing approaches to Knowledge Base Question Answering focus on semantic parsing . previous work focused on selecting the correct semantic relations and not on the structure of the semantic parses . |
| Approach: | They propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse. |
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Simple and Effective Semi-Supervised Question Answering (N18-2)
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| Challenge: | Existing deep learning systems for extractive Question Answering are limited and expensive to construct. |
| Approach: | They propose a semi-supervised QA system where end user specifies a set of documents and only a few labelled examples. |
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A Recurrent BERT-based Model for Question Generation (D19-58)
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| Challenge: | Existing QG models rely on recurrent neural networks (RNNs) but the inherent sequential nature of the RNN models suffers from the problem of handling long sequences. |
| Approach: | They propose to employ a pre-trained BERT language model to tackle question generation tasks. |
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HPE: Answering Complex Questions over Text by Hybrid Question Parsing and Execution (2023.findings-emnlp)
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| Challenge: | End-to-end neural networks excel at answering natural language questions but fail on complex ones . a proposed framework for question parsing and execution on textual QA is designed to combine the strengths of neural and symbolic methods. |
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Talk to Papers: Bringing Neural Question Answering to Academic Search (2020.acl-demos)
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| Challenge: | Talk to Papers aims to improve the current experience of academic search by using open-domain question answering (QA) techniques. |
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Interactive Query-Assisted Summarization via Deep Reinforcement Learning (2022.naacl-main)
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| Challenge: | Existing systems that can perform interactive summarization cannot ingest the full document set or operate at sufficient speed for interactivity. |
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CNN for Text-Based Multiple Choice Question Answering (P18-2)
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| Challenge: | Existing models for text-based multiple choice question answering are based on a text. |
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Dependent Gated Reading for Cloze-Style Question Answering (C18-1)
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Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervision (2024.emnlp-main)
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| Challenge: | Cross-lingual open domain question answering requires multiple models, requiring substantial annotated datasets and auxiliary resources to bridge between languages. |
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Complex Question Answering on knowledge graphs using machine translation and multi-task learning (2021.eacl-main)
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Saurabh Srivastava, Mayur Patidar, Sudip Chowdhury, Puneet Agarwal, Indrajit Bhattacharya, Gautam Shroff
| Challenge: | Existing approaches to question answering on knowledge graphs are based on a modularized sequential approach where errors in one module lead to the accumulation of errors in downstream modules. |
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| Outcome: | The proposed model can answer questions over a knowledge graph on one publicly available and one proprietary dataset. |