Hypothesis Only Baselines in Natural Language Inference
The paper "Hypothesis Only Baselines in Natural Language Inference" addresses the examination and evaluation of Natural Language Inference (NLI) datasets by investigating the performance of models that leverage only the hypothesis component, excluding the context or premise. The authors propose a novel baseline approach—hypothesis-only models—to provide a clearer diagnostic framework for understanding potential biases and statistical irregularities in NLI datasets.
Key Contributions
The authors explore whether it is feasible for models to accurately perform inference tasks without any context, which traditionally should be essential to NLI. Through empirical studies conducted across ten distinct NLI datasets, they establish that hypothesis-only models often exceed the performance of majority-class baselines. This finding prompts a closer scrutiny of NLI datasets to uncover underlying characteristics that enable models to benefit from such minimal input.
Methodology
The paper introduces a hypothesis-only baseline, derived from a modification of the InferSent method, which is typically employed in NLI tasks. This modification involves adapting InferSent’s BiLSTM encoder to process only hypothesis sentences, disregarding the premises entirely. The paper encompasses an analysis on a wide array of datasets, categorized into three groups based on the dataset creation methodology: human elicited, human judged, and automatically recast datasets.
Results and Analysis
Significant results include:
- The hypothesis-only baselines surpass the majority class baseline in several datasets, particularly in those constructed through human elicitation (e.g., SNLI and Multi-NLI), where they more than double the majority baseline performance.
- The paper identifies that lexically charged words, grammatical patterns, and semantic properties related to the lexical distributions contribute to "giveaway" features that the models exploit.
- It suggests that existing dataset construction processes may introduce biases that can be leveraged by models without truly understanding the linguistic relationship between the premises and hypotheses.
Implications and Future Directions
The findings emphasize the need for designing NLI datasets that are robust against exploitation by hypothesis-only reasoning, ensuring models genuinely comprehend and utilize the full input structure. This paper calls for future NLI models to benchmark against this hypothesis-only baseline to validate that their understanding extends beyond dataset-specific annotations.
Moreover, the implications extend towards more conscious dataset creation processes and consideration in multi-modal tasks. The fact that simple lexical cues can disproportionately enhance model performance underlines the importance of developing NLI benchmarks that genuinely reflect nuanced linguistic understanding.
The discussion encourages researchers to apply similar baseline methodologies in related domains, such as Visual QA, and suggests that multimodal tasks may exhibit similar vulnerabilities. Addressing these issues would advance the reliability and validity of models claiming to achieve human-level language understanding.
In summary, this paper provides a critical perspective on NLI task formulation and advocates for better evaluation practices in the design of language inference models, thus contributing to the discourse on the reliability and interpretability of AI systems in natural language understanding.