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DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension
TLDR
We present DREAM, the first dialogue-based multiple-choice reading comprehension data set. Expand
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Improving Machine Reading Comprehension with General Reading Strategies
TLDR
We propose three general strategies aimed to improve non-extractive machine reading comprehension (MRC): (i) BACK AND FORTH READING that considers both the original and reverse order of an input sequence, (ii) HIGHLIGHTING, which adds a trainable embedding to the text embedding of tokens that are relevant to the question and candidate answer, and (iii) SELF-ASSESSMENT that generates practice questions and candidate answers directly from the text. Expand
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Evidence Sentence Extraction for Machine Reading Comprehension
TLDR
We focus on extracting evidence sentences that can explain or support the answers of multiple-choice MRC tasks, where the majority of answer options cannot be directly extracted from reference documents. Expand
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A generalized rule based tracker for dialogue state tracking
TLDR
A novel framework is proposed to formulate rule-based models in a general way. Expand
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Probing Prior Knowledge Needed in Challenging Chinese Machine Reading Comprehension
TLDR
We present the first collection of Challenging Chinese machine reading Comprehension datasets (C^3) collected from language and professional certification exams, which contains 13,924 documents and their associated 23,990 multiple-choice questions. Expand
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Cornell Belief and Sentiment System at TAC 2017
TLDR
In this paper we describe the 2017 system of the CornMich team for the TAC Belief and Sentiment (BeSt) task for Chinese. Expand
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The SJTU System for Dialog State Tracking Challenge 2
TLDR
This paper describes the SJTU system submitted to the second Dialogue State Tracking Challenge in detail. Expand
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Constrained Markov Bayesian Polynomial for Efficient Dialogue State Tracking
TLDR
In this paper, a novel hybrid framework, constrained Markov Bayesian polynomial (CMBP), is proposed to formulate rule-based DST in a general way and allow data-driven rule generation. Expand
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CLUE: A Chinese Language Understanding Evaluation Benchmark
TLDR
We introduce CLUE, a Chinese Language Understanding Evaluation benchmark that contains eight different natural language understanding tasks, including single-sentence classification, sentence pair classification, and machine reading comprehension. Expand
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Dialogue-Based Relation Extraction
TLDR
We present the first human-annotated dialogue-based relation extraction dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. Expand
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