Papers with CommitmentBank

4 papers
Comparing Test Sets with Item Response Theory (2021.acl-long)

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Challenge: Recent results from large pretrained models show that many datasets are saturated and unlikely to detect further progress.
Approach: They evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples.
Outcome: The proposed datasets are saturated and unlikely to detect future improvements.
Identifying inherent disagreement in natural language inference (2021.naacl-main)

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Challenge: Natural language inference is the task of determining whether text is entailed, contradicted or unrelated to another piece of text.
Approach: They propose to tease systematic inferences from disagreement items by capturing modes in annotations to simulate uncertainty in the annotation process.
Outcome: The proposed approach performs statistically better than baselines on the CommitmentBank corpus in English.
Do You Know That Florence Is Packed with Visitors? Evaluating State-of-the-art Models of Speaker Commitment (P19-1)

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Challenge: Existing models for speaker commitment fail to generalize to diverse linguistic constructions, highlighting directions for improvement.
Approach: They evaluate two state-of-the-art speaker commitment models on the CommitmentBank . they analyze linguistic correlates of model error on a naturalistic dataset .
Outcome: The proposed models perform well on some classes but fail to generalize to diverse linguistic constructions.
Evaluating BERT for natural language inference: A case study on the CommitmentBank (D19-1)

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Challenge: Natural language inference datasets can identify premise-hypothesis relationship without observing premise . recasting of the CommitmentBank for NLI creates hypotheses that stand in entailment/contradiction/neutral relationship with premise.
Approach: They propose to recast the CommitmentBank for NLI to stand in certain relationships with the premise . hypotheses are complements of clause-embedding verbs in each premise, rethinking the CommittedBank .
Outcome: The proposed model performs well on the CommitmentBank with 85% F1 . however, the model does not capture the full complexity of pragmatic reasoning, authors say .

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