1 Introduction Natural language inference (NLI) is a core challenge in the … We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). A success-ful partial-input baseline indicates that a dataset contains artifacts which make it easier than ex-pected.

Natural language inference (NLI; also known as recognizing textual entailment, or RTE) is a widely-studied task in natural language processing, to which many complex semantic tasks, such as question answering and text summarization, can be reduced . Perspectives • Zaenen et al.

In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics . Abstract: Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise. • Other groups have since further supported this (Poliak et al.

One solution is cross-lingual … Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. … • Focus on local inference steps, rather than long deductive chains. Association for Computational Linguistics New Orleans, Louisiana conference publication We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). ().Given a pair of sentences, a premise p and a hypothesis h, the goal is to determine whether or not p semantically entails h.

Abstract: Add/Edit. Hypothesis-only baselines • In his project for this course (2016), Leonid Keselman observed that hypothesis-only models are strong. These artefacts are exploited by neural networks even when only considering the hypothesis and ignoring the premise, leading to unwanted biases. (2005): Local textual inference: can it be defined or circumscribed? (2018) found that the Stanford Natural Language Inference dataset (SNLI;Bowman et al., 2015) contained the most (or worst) hypothesis-only biases—their hypothesis-only model outper-formed the majority baseline by roughly 100% (going from roughly 34% to 69%). In this work, we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. The corpus is made to evaluate how to perform inference in any language (including low-resources ones like Swahili or Urdu) when only English NLI data is available at training time.

We present a new logic-based inference engine for natural language inference (NLI) called MonaLog, which is based on natural logic and the monotonicity calculus. Abstract We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI).

Especially when an NLI dataset assumes infer-ence is occurring based purely on the relation-ship between a context and a hypothesis, it fol-lows that assessing entailment relations while ignoring the provided context is a degenerate solution. In this work, we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Our analyses indicate that the representations … Does the premise justify an inference to the hypothesis?