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Idiomatic Expression Identification using Semantic Compatibility
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1546-1562 (2021) (2021)
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| 82 |
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 176-194 (2021) (2021)
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| 83 |
Reducing Confusion in Active Learning for Part-Of-Speech Tagging
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1-16 (2021) (2021)
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| 84 |
Differentiable Subset Pruning of Transformer Heads
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1442-1459 (2021) (2021)
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| 85 |
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1061-1080 (2021) (2021)
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| 86 |
Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 410-428 (2021) (2021)
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| 87 |
Data-to-text Generation with Macro Planning
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 510-527 (2021) (2021)
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| 88 |
Structured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1268-1284 (2021) (2021)
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| 89 |
RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases
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In: Computational Linguistics, Vol 47, Iss 2, Pp 309-332 (2021) (2021)
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| 90 |
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1032-1046 (2021) (2021)
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| 91 |
Maintaining Common Ground in Dynamic Environments
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 995-1011 (2021) (2021)
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| 92 |
Infusing Finetuning with Semantic Dependencies
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 226-242 (2021) (2021)
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| 93 |
On Generative Spoken Language Modeling from Raw Audio
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1336-1354 (2021) (2021)
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| 94 |
Pretraining the Noisy Channel Model for Task-Oriented Dialogue
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 657-674 (2021) (2021)
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| 95 |
Approximating Probabilistic Models as Weighted Finite Automata
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In: Computational Linguistics, Vol 47, Iss 2, Pp 221-254 (2021) (2021)
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| 96 |
Sensitivity as a Complexity Measure for Sequence Classification Tasks
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 891-908 (2021) (2021)
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| 97 |
Unsupervised Learning of KB Queries in Task-Oriented Dialogs
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 374-390 (2021) (2021)
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| 98 |
<scp>ParsiNLU</scp>: A Suite of Language Understanding Challenges for Persian
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1147-1162 (2021) (2021)
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Abstract:
AbstractDespite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce ParsiNLU, the first benchmark in Persian language that includes a range of language understanding tasks—reading comprehension, textual entailment, and so on. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5k new instances across 6 distinct NLU tasks. Additionally, we present the first results on state-of-the-art monolingual and multilingual pre-trained language models on this benchmark and compare them with human performance, which provides valuable insights into our ability to tackle natural language understanding challenges in Persian. We hope ParsiNLU fosters further research and advances in Persian language understanding.1
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Keyword:
Computational linguistics. Natural language processing; P98-98.5
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URL: https://doaj.org/article/628c3c423b8e4fc681edcc882bbb9c33
https://doi.org/10.1162/tacl_a_00419
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| 99 |
Adaptive Semiparametric Language Models
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 362-373 (2021) (2021)
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| 100 |
Strong Equivalence of TAG and CCG
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 707-720 (2021) (2021)
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