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

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

10 Dec 2018arXiv:1812.03593archive 2025-07-28

Chenguang Zhu, Michael Zeng, Xuedong Huang

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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Code

Microsoft/SDNet officialmentioned on GitHubpytorch report
gooofy/zbrain mentioned on GitHubtf report
mpandeydev/SDnetmod mentioned on GitHubpytorch report

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Tasks

Conversational Question AnsweringCoreference ResolutionMachine Reading ComprehensionQuestion AnsweringReading ComprehensionSpoken Language Understanding

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering CoQA SDNet (ensemble) Overall 79.3 #8 of 9 Archive leaderboard report
Question Answering CoQA SDNet (single model) Overall 76.6 #9 of 9 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AdamAttentionAttention DropoutBERTDense ConnectionsDropoutLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionResidual ConnectionSoftmaxWeight DecayWordPiece

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