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Hits 81 – 100 of 1.255

81
How effective is BERT without word ordering? Implications for language understanding and data privacy ...
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82
GEM: Natural Language Generation, Evaluation, and Metrics - Part 4 ...
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83
The statistical advantage of automatic NLG metrics at the system level ...
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84
Counter-Argument Generation by Attacking Weak Premises ...
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85
Supporting Cognitive and Emotional Empathic Writing of Students ...
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86
What's in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus ...
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87
Are Pretrained Convolutions Better than Pretrained Transformers? ...
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88
Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? ...
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89
Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring ...
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90
Hate Speech Detection Based on Sentiment Knowledge Sharing ...
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91
Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation ...
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92
Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction ...
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93
WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation ...
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94
An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization ...
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95
How does Attention Affect the Model? ...
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96
Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance ...
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97
Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities ...
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98
Neural Machine Translation with Monolingual Translation Memory ...
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99
Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems ...
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100
Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.255 Abstract: Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking. Our experiments on two datasets demonstrate the effectiveness of our proposed method. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/yv1r-8040
https://underline.io/lecture/25590-modeling-transitions-of-focal-entities-for-conversational-knowledge-base-question-answering
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