Papers with downstream natural language understanding tasks
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations (P18-1)
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| Challenge: | Using neural machine translation, we generate more than 50 million sentential paraphrase pairs from a large parallel corpus. |
| Approach: | They use a dataset of more than 50 million English-English sentential paraphrase pairs to generate them automatically using neural machine translation. |
| Outcome: | The proposed dataset outperforms all supervised systems on every SemEval semantic textual similarity competition and shows how it can be used for paraphrase generation. |
Sentences with Gapping: Parsing and Reconstructing Elided Predicates (N18-1)
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| Challenge: | Sentences with gapping lack an overt predicate to indicate the relation between two or more arguments. |
| Approach: | They propose two methods for parsing to a Universal Dependencies graph representation that explicitly encodes the elided material with additional nodes and edges. |
| Outcome: | The proposed methods reconstruct elided material from dependency trees with high accuracy when the parser correctly predicts the existence of a gap. |
Aff2Vec: Affect–Enriched Distributional Word Representations (C18-1)
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| Challenge: | Affective word distributions are not well understood in literature. |
| Approach: | They propose a model that embeds affective word interpretations into enriched word embeddings. |
| Outcome: | The proposed model outperforms the state-of-the-art in word-similarity tasks and in emotion analysis, personality detection, and frustration prediction tasks. |
Defending Pre-trained Language Models from Adversarial Word Substitution Without Performance Sacrifice (2021.findings-acl)
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| Challenge: | Existing defense approaches suffer from notable performance loss and complexities. |
| Approach: | They propose a framework for detecting anomalies with frequency-aware randomization to defend adversarial word substitution. |
| Outcome: | The proposed framework outperforms existing defense methods over various tasks with much higher inference speed. |
LET: Leveraging Error Type Information for Grammatical Error Correction (2023.findings-acl)
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| Challenge: | Existing methods for grammatical error correction (GEC) are mainly divided into detection-based and end-to-end generative models. |
| Approach: | They propose an end-to-end framework which Leverages Error Type (LET) information in the generation process to introduce more convincing error type information. |
| Outcome: | The proposed framework outperforms existing methods on various datasets by a clear margin. |
PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer (2023.emnlp-main)
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| Challenge: | Existing prompt tuning methods have training instability issues due to large variance of scores . existing prompt tuning algorithms have training stability issues due a slight change of input data . |
| Approach: | They propose an algorithm that smooths the loss landscape of vanilla prompt tuning by perturbation-based regularizers. |
| Outcome: | The proposed method improves the state-of-the-art prompt tuning methods by 1.94% and 2.34% on SuperGLUE and FewGLUE benchmarks. |