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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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Abstract:
AbstractFor natural language processing systems, two kinds of evidence support the use of text representations from neural language models “pretrained” on large unannotated corpora: performance on application-inspired benchmarks (Peters et al., 2018, inter alia), and the emergence of syntactic abstractions in those representations (Tenney et al., 2019, inter alia). On the other hand, the lack of grounded supervision calls into question how well these representations can ever capture meaning (Bender and Koller, 2020). We apply novel probes to recent language models— specifically focusing on predicate-argument structure as operationalized by semantic dependencies (Ivanova et al., 2012)—and find that, unlike syntax, semantics is not brought to the surface by today’s pretrained models. We then use convolutional graph encoders to explicitly incorporate semantic parses into task-specific finetuning, yielding benefits to natural language understanding (NLU) tasks in the GLUE benchmark. This approach demonstrates the potential for general-purpose (rather than task-specific) linguistic supervision, above and beyond conventional pretraining and finetuning. Several diagnostics help to localize the benefits of our approach.1
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Keyword:
Computational linguistics. Natural language processing; P98-98.5
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URL: https://doaj.org/article/9d3ce8a325be4786bb709bb07473acbf https://doi.org/10.1162/tacl_a_00363
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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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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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<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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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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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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