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Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes ...
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102 |
Wikily Supervised Neural Translation Tailored to Cross-Lingual Tasks ...
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103 |
Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization ...
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104 |
Sorting through the noise: Testing robustness of information processing in pre-trained language models ...
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105 |
Building the Directed Semantic Graph for Coherent Long Text Generation ...
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106 |
Detect and Classify – Joint Span Detection and Classification for Health Outcomes ...
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107 |
Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation ...
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108 |
Evaluation of Summarization Systems across Gender, Age, and Race ...
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109 |
A Language Model-based Generative Classifier for Sentence-level Discourse Parsing ...
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110 |
Controllable Neural Dialogue Summarization with Personal Named Entity Planning ...
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112 |
Graphine: A Dataset for Graph-aware Terminology Definition Generation ...
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113 |
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization ...
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114 |
Connecting Attributions and QA Model Behavior on Realistic Counterfactuals ...
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115 |
Provable Limitations of Acquiring Meaning from Ungrounded Form: What will Future Language Models Understand? ...
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116 |
Generation and Extraction Combined Dialogue State Tracking with Hierarchical Ontology Integration ...
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117 |
Error-Sensitive Evaluation for Ordinal Target Variables ...
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118 |
CDLM: Cross-Document Language Modeling ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.findings-emnlp.225.pdf Abstract: We introduce a new pretraining approach geared for multi-document language modeling, incorporating two key ideas into the masked language modeling self-supervised objective. First, instead of considering documents in isolation, we pretrain over sets of multiple related documents, encouraging the model to learn cross-document relationships. Second, we improve over recent long-range transformers by introducing dynamic global attention that has access to the entire input to predict masked tokens. We release CDLM (Cross-Document Language Model), a new general language model for multi-document setting that can be easily applied to downstream tasks. Our extensive analysis shows that both ideas are essential for the success of CDLM, and work in synergy to set new state-of-the-art results for several multi-text tasks. ...
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Keyword:
Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/pbq6-k224 https://underline.io/lecture/39791-cdlm-cross-document-language-modeling
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119 |
Data-to-text Generation by Splicing Together Nearest Neighbors ...
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120 |
Natural Language Processing Meets Quantum Physics: A Survey and Categorization ...
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