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1
Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings ...
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2
Improving Factual Consistency of Abstractive Summarization via Question Answering ...
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3
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection ...
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4
A Bag of Tricks for Dialogue Summarization ...
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5
Event-Centric Natural Language Processing ...
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6
Cross-language Sentence Selection via Data Augmentation and Rationale Training ...
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7
Event Guided Denoising for Multilingual Relation Learning ...
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8
WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization ...
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9
Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages ...
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10
Event-Guided Denoising for Multilingual Relation Learning ...
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11
Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations ...
Allaway, Emily; McKeown, Kathleen. - : arXiv, 2020
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12
Incorporating Terminology Constraints in Automatic Post-Editing ...
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13
A Unified Feature Representation for Lexical Connotations ...
Allaway, Emily; McKeown, Kathleen. - : arXiv, 2020
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14
Subtitles to Segmentation: Improving Low-Resource Speech-to-Text Translation Pipelines ...
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15
Contextual Analysis of Social Media: The Promise and Challenge of Eliciting Context in Social Media Posts with Natural Language Processing
In: Proc AAAI ACM Conf AI Ethics Soc (2020)
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16
SMARTies: Sentiment Models for Arabic Target Entities ...
Farra, Noura; McKeown, Kathleen. - : arXiv, 2017
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17
Aggregated Word Pair Features for Implicit Discourse Relation Disambiguation
Biran, Or; McKeown, Kathleen. - : The Association for Computational Linguistics, 2013
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18
Aggregated Word Pair Features for Implicit Discourse Relation Disambiguation ...
Biran, Or; McKeown, Kathleen. - : Columbia University, 2013
Abstract: We present a reformulation of the word pair features typically used for the task of disambiguating implicit relations in the Penn Discourse Treebank. Our word pair features achieve significantly higher performance than the previous formulation when evaluated without additional features. In addition, we present results for a full system using additional features which achieves close to state of the art performance without resorting to gold syntactic parses or to context outside the relation. ...
Keyword: Computer science; FOS Languages and literature; Linguistics
URL: https://dx.doi.org/10.7916/d8pn9fz4
https://academiccommons.columbia.edu/doi/10.7916/D8PN9FZ4
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19
Detecting and Correcting Syntactic Errors in Machine Translation Using Feature-Based Lexicalized Tree Adjoining Grammars
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20
Detecting and Correcting Syntactic Errors in Machine Translation Using Feature-Based Lexicalized Tree Adjoining Grammars
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