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SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering

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arxiv 1812.03593 v5 pith:TO47SJA4 submitted 2018-12-10 cs.CL

Chenguang Zhu , Michael Zeng , Xuedong Huang This is my paper · ORCID
classification cs.CL
keywords modelcontextansweringattention-basedcomprehensioncontextualcontextualizedconversational
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Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context. Different from traditional single-turn machine reading comprehension (MRC) tasks, CQA includes passage comprehension, coreference resolution, and contextual understanding. In this paper, we propose an innovated contextualized attention-based deep neural network, SDNet, to fuse context into traditional MRC models. Our model leverages both inter-attention and self-attention to comprehend conversation context and extract relevant information from passage. Furthermore, we demonstrated a novel method to integrate the latest BERT contextual model. Empirical results show the effectiveness of our model, which sets the new state of the art result in CoQA leaderboard, outperforming the previous best model by 1.6% F1. Our ensemble model further improves the result by 2.7% F1.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Building Open-Retrieval Conversational Question Answering Systems by Generating Synthetic Data and Decontextualizing User Questions

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A pipeline that extracts propositions from documents, generates synthetic grounded dialogs, and uses them to train lightweight question rewriters and retrievers for open-retrieval conversational QA.

  2. Patient Knowledge Distillation for BERT Model Compression

    cs.CL 2019-08 conditional novelty 5.0 of 10

    Distilling BERT through several intermediate hidden layers (Patient-KD) improves a shallow student's accuracy on GLUE and RACE compared with last-layer-only distillation.

  3. Dialog State Tracking: A Neural Reading Comprehension Approach

    cs.CL 2019-08 conditional novelty 5.0 of 10

    An attention-based reading comprehension model that points to slot values in the conversation, augmented by an explicit slot carryover model and a fixed-vocabulary hybrid, sets a new state-of-the-art on MultiWOZ-2.0 d...

  4. FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension

    cs.CL 2019-08 conditional novelty 4.0 of 10

    Modeling the difference between consecutive reasoning states, called FlowDelta, improves conversational machine comprehension accuracy across FlowQA and BERT on CoQA, QuAC, and SCONE.

  5. A Survey of the State-of-the-Art in Conversational Question Answering Systems

    cs.CL 2025-09 conditional novelty 2.0 of 10

    A review that categorizes ConvQA components, techniques, models, and datasets, with no new experimental result.

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