Evaluating Saliency Methods for Neural Language Models

• URL: http://arxiv.org/abs/2104.05824v1
• Date: Mon, 12 Apr 2021 21:19:48 GMT
• Title: Evaluating Saliency Methods for Neural Language Models
• Authors: Shuoyang Ding, Philipp Koehn
• Abstract summary: Saliency methods are widely used to interpret neural network predictions. Different variants of saliency methods disagree even on the interpretations of the same prediction made by the same model. We conduct a comprehensive and quantitative evaluation of saliency methods on a fundamental category of NLP models: neural language models.
• Score: 9.309351023703018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Saliency methods are widely used to interpret neural network predictions, but different variants of saliency methods often disagree even on the interpretations of the same prediction made by the same model. In these cases, how do we identify when are these interpretations trustworthy enough to be used in analyses? To address this question, we conduct a comprehensive and quantitative evaluation of saliency methods on a fundamental category of NLP models: neural language models. We evaluate the quality of prediction interpretations from two perspectives that each represents a desirable property of these interpretations: plausibility and faithfulness. Our evaluation is conducted on four different datasets constructed from the existing human annotation of syntactic and semantic agreements, on both sentence-level and document-level. Through our evaluation, we identified various ways saliency methods could yield interpretations of low quality. We recommend that future work deploying such methods to neural language models should carefully validate their interpretations before drawing insights.

Related papers

• Constructing Word-Context-Coupled Space Aligned with Associative Knowledge Relations for Interpretable Language Modeling [0.0]
  The black-box structure of the deep neural network in pre-trained language models seriously limits the interpretability of the language modeling process. A Word-Context-Coupled Space (W2CSpace) is proposed by introducing the alignment processing between uninterpretable neural representation and interpretable statistical logic. Our language model can achieve better performance and highly credible interpretable ability compared to related state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-19T09:26:02Z)
• A Unified Understanding of Deep NLP Models for Text Classification [88.35418976241057]
  We have developed a visual analysis tool, DeepNLPVis, to enable a unified understanding of NLP models for text classification. The key idea is a mutual information-based measure, which provides quantitative explanations on how each layer of a model maintains the information of input words in a sample. A multi-level visualization, which consists of a corpus-level, a sample-level, and a word-level visualization, supports the analysis from the overall training set to individual samples.
  arXiv  Detail & Related papers  (2022-06-19T08:55:07Z)
• A Fine-grained Interpretability Evaluation Benchmark for Neural NLP [44.08113828762984]
  This benchmark covers three representative NLP tasks: sentiment analysis, textual similarity and reading comprehension. We provide token-level rationales that are carefully annotated to be sufficient, compact and comprehensive. We conduct experiments on three typical models with three saliency methods, and unveil their strengths and weakness in terms of interpretability.
  arXiv  Detail & Related papers  (2022-05-23T07:37:04Z)
• Learnable Visual Words for Interpretable Image Recognition [70.85686267987744]
  We propose the Learnable Visual Words (LVW) to interpret the model prediction behaviors with two novel modules. The semantic visual words learning relaxes the category-specific constraint, enabling the general visual words shared across different categories. Our experiments on six visual benchmarks demonstrate the superior effectiveness of our proposed LVW in both accuracy and model interpretation.
  arXiv  Detail & Related papers  (2022-05-22T03:24:45Z)
• Hierarchical Interpretation of Neural Text Classification [31.95426448656938]
  This paper proposes a novel Hierarchical INTerpretable neural text classifier, called Hint, which can automatically generate explanations of model predictions. Experimental results on both review datasets and news datasets show that our proposed approach achieves text classification results on par with existing state-of-the-art text classifiers.
  arXiv  Detail & Related papers  (2022-02-20T11:15:03Z)
• A Latent-Variable Model for Intrinsic Probing [93.62808331764072]
  We propose a novel latent-variable formulation for constructing intrinsic probes. We find empirical evidence that pre-trained representations develop a cross-lingually entangled notion of morphosyntax.
  arXiv  Detail & Related papers  (2022-01-20T15:01:12Z)
• On the Lack of Robust Interpretability of Neural Text Classifiers [14.685352584216757]
  We assess the robustness of interpretations of neural text classifiers based on pretrained Transformer encoders. Both tests show surprising deviations from expected behavior, raising questions about the extent of insights that practitioners may draw from interpretations.
  arXiv  Detail & Related papers  (2021-06-08T18:31:02Z)
• On the Faithfulness Measurements for Model Interpretations [100.2730234575114]
  Post-hoc interpretations aim to uncover how natural language processing (NLP) models make predictions. To tackle these issues, we start with three criteria: the removal-based criterion, the sensitivity of interpretations, and the stability of interpretations. Motivated by the desideratum of these faithfulness notions, we introduce a new class of interpretation methods that adopt techniques from the adversarial domain.
  arXiv  Detail & Related papers  (2021-04-18T09:19:44Z)
• Interpretable Deep Learning: Interpretations, Interpretability, Trustworthiness, and Beyond [49.93153180169685]
  We introduce and clarify two basic concepts-interpretations and interpretability-that people usually get confused. We elaborate the design of several recent interpretation algorithms, from different perspectives, through proposing a new taxonomy. We summarize the existing work in evaluating models' interpretability using "trustworthy" interpretation algorithms.
  arXiv  Detail & Related papers  (2021-03-19T08:40:30Z)
• Interpretable Multi-dataset Evaluation for Named Entity Recognition [110.64368106131062]
  We present a general methodology for interpretable evaluation for the named entity recognition (NER) task. The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them. By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area.
  arXiv  Detail & Related papers  (2020-11-13T10:53:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.