27 papers with code • 1 benchmarks • 1 datasets

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# Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering

21 Jul 2016

While question answering (QA) with neural network, i. e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge for developing and evaluating neural QA system.

3

# Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

27 Jan 2020

In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions.

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# LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering

21 Nov 2020

We show that LRTA makes a step towards truly understanding the question while the state-of-the-art model tends to learn superficial correlations from the training data.

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8 Sep 2021

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# R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering

31 Aug 2017

Second, we propose a novel method that jointly trains the Ranker along with an answer-generation Reader model, based on reinforcement learning.

1

23 Jan 2019

In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.

1

# Review-Driven Answer Generation for Product-Related Questions in E-Commerce

27 Apr 2019

Then, we devise a mechanism to identify the relevant information from the noise-prone review snippets and incorporate this information to guide the answer generation.

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12 Aug 2019

Observing that many questions can be answered based upon the available product reviews, we propose the task of review-based QA.

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