ACL 2019.
Code-mixing is the use of more than one language in the same conversation or utterance, and is prevalent in multilingual communities all over the world.
R. Thomas McCoy, Robert Frank, and Tal Linzen. link: 10.10.2019: Adam Tsakalidis: Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference, McCoy et al.
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference., McCoy et al., 2019 #nlp #linguistics #datasets Linguistic Knowledge and Transferability of Contextual Representations, Liu et al., 2019 [ Paper ] [ Notes ] #nlp Robust Natural Language Inference Models with Example Forgetting. Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference Machine learning systems can often achieve high performance on a test set by relying on heuristics that are effective for frequent example types but break down in more challenging cases. 78: 2019: [ ACL anthology ] [ pdf ] This repository contains the HANS (Heuristic Analysis for NLI Systems) dataset. Large neural models have demonstrated human-level performance on language and vision benchmarks such as ImageNet and Stanford Natural Language Inference (SNLI). We investigate whether example forgetting, a recently introduced measure of hardness of examples, can be used to select training examples in order to increase robustness of natural language understanding models in a natural language inference … In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics .
RT McCoy, E Pavlick, T Linzen. Natural Language Inference (NLI) is the task of determining the semantic relationship between a premise and a hypothesis.
Natural Language Inference (NLI) is the task of inferring the logical relationship, typically entailment or contradiction, between a premise and hypothesis. HANS.
∙ Microsoft ∙ 0 ∙ share . Large neural models have demonstrated human-level performance on language and vision benchmarks such as ImageNet and Stanford Natural Language Inference (SNLI). Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. The meetings are organised by ... Mitigating Artifacts in Natural Language Inference, Belinkov et al.
Based on an analysis of the task, we hypothesize three fallible syntactic heuristics that NLI models are likely to adopt: the lexical overlap heuristic, the subsequence heuristic, and the constituent heuristic.
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference. Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference. Yet, their performance degrades considerably when tested on adversarial or out-of-distribution samples. CoRR abs/1902.01007) HANS Result with Augmented Dataset