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1
Incorporating Constituent Syntax for Coreference Resolution ...
Jiang, Fan; Cohn, Trevor. - : arXiv, 2022
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2
PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation ...
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3
Fairness-aware Class Imbalanced Learning ...
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4
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation ...
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5
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning ...
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6
It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data ...
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7
Generating Diverse Descriptions from Semantic Graphs ...
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8
Balancing out Bias: Achieving Fairness Through Training Reweighting ...
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9
ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents
In: Front Res Metr Anal (2021)
Abstract: Chemical patents represent a valuable source of information about new chemical compounds, which is critical to the drug discovery process. Automated information extraction over chemical patents is, however, a challenging task due to the large volume of existing patents and the complex linguistic properties of chemical patents. The Cheminformatics Elsevier Melbourne University (ChEMU) evaluation lab 2020, part of the Conference and Labs of the Evaluation Forum 2020 (CLEF2020), was introduced to support the development of advanced text mining techniques for chemical patents. The ChEMU 2020 lab proposed two fundamental information extraction tasks focusing on chemical reaction processes described in chemical patents: (1) chemical named entity recognition, requiring identification of essential chemical entities and their roles in chemical reactions, as well as reaction conditions; and (2) event extraction, which aims at identification of event steps relating the entities involved in chemical reactions. The ChEMU 2020 lab received 37 team registrations and 46 runs. Overall, the performance of submissions for these tasks exceeded our expectations, with the top systems outperforming strong baselines. We further show the methods to be robust to variations in sampling of the test data. We provide a detailed overview of the ChEMU 2020 corpus and its annotation, showing that inter-annotator agreement is very strong. We also present the methods adopted by participants, provide a detailed analysis of their performance, and carefully consider the potential impact of data leakage on interpretation of the results. The ChEMU 2020 Lab has shown the viability of automated methods to support information extraction of key information in chemical patents.
Keyword: Research Metrics and Analytics
URL: https://doi.org/10.3389/frma.2021.654438
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028406/
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10
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning ...
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11
Please Mind the Root: Decoding Arborescences for Dependency Parsing
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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12
Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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13
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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14
Learning a Cost-Effective Annotation Policy for Question Answering
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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15
Pareto Probing: Trading Off Accuracy for Complexity
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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16
Speakers Fill Lexical Semantic Gaps with Context
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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17
Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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18
Intrinsic Probing through Dimension Selection
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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19
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation
In: Findings of the Association for Computational Linguistics: EMNLP 2020 (2020)
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20
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets
Sutcliffe, Richard; Samothrakis, Spyridon; Quteineh, Husam. - : Association for Computational Linguistics, 2020
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