Main Predicate and Their Arguments as Explanation Signals For Intent Classification

• URL: http://arxiv.org/abs/2502.01270v1
• Date: Mon, 03 Feb 2025 11:39:26 GMT
• Title: Main Predicate and Their Arguments as Explanation Signals For Intent Classification
• Authors: Sameer Pimparkhede, Pushpak Bhattacharyya,
• Abstract summary: We show that the main verb denotes the action, and the direct object indicates the domain of conversation, serving as explanation signals for intent. We mark main predicates (primarily verbs) and their arguments as explanation signals in benchmark intent classification datasets ATIS and SNIPS. We observe that models that work well for classification do not perform well in explainability metrics like plausibility and faithfulness.
• Score: 41.09752906121257
• License:
• Abstract: Intent classification is crucial for conversational agents (chatbots), and deep learning models perform well in this area. However, little research has been done on the explainability of intent classification due to the absence of suitable benchmark data. Human annotation of explanation signals in text samples is time-consuming and costly. However, from inspection of data on intent classification, we see that, more often than not, the main verb denotes the action, and the direct object indicates the domain of conversation, serving as explanation signals for intent. This observation enables us to hypothesize that the main predicate in the text utterances, along with the arguments of the main predicate, can serve as explanation signals. Leveraging this, we introduce a new technique to automatically augment text samples from intent classification datasets with word-level explanations. We mark main predicates (primarily verbs) and their arguments (dependency relations) as explanation signals in benchmark intent classification datasets ATIS and SNIPS, creating a unique 21k-instance dataset for explainability. Further, we experiment with deep learning and language models. We observe that models that work well for classification do not perform well in explainability metrics like plausibility and faithfulness. We also observe that guiding models to focus on explanation signals from our dataset during training improves the plausibility Token F1 score by 3-4%, improving the model's reasoning.

Related papers

• Zero-Shot Text Classification via Self-Supervised Tuning [46.9902502503747]
  We propose a new paradigm based on self-supervised learning to solve zero-shot text classification tasks. tuning the language models with unlabeled data, called self-supervised tuning. Our model outperforms the state-of-the-art baselines on 7 out of 10 tasks.
  arXiv  Detail & Related papers  (2023-05-19T05:47:33Z)
• What Are You Token About? Dense Retrieval as Distributions Over the Vocabulary [68.77983831618685]
  We propose to interpret the vector representations produced by dual encoders by projecting them into the model's vocabulary space. We show that the resulting projections contain rich semantic information, and draw connection between them and sparse retrieval.
  arXiv  Detail & Related papers  (2022-12-20T16:03:25Z)
• Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods [6.018950511093273]
  Saliency maps can explain a neural model's predictions by identifying important input features. We formalize the underexplored task of translating saliency maps into natural language. We compare two novel methods (search-based and instruction-based verbalizations) against conventional feature importance representations.
  arXiv  Detail & Related papers  (2022-10-13T17:48:15Z)
• Perturbations and Subpopulations for Testing Robustness in Token-Based Argument Unit Recognition [6.502694770864571]
  Argument Unit Recognition and Classification aims at identifying argument units from text and classifying them as pro or against. One of the design choices that need to be made when developing systems for this task is what the unit of classification should be: segments of tokens or full sentences. Previous research suggests that fine-tuning language models on the token-level yields more robust results for classifying sentences compared to training on sentences directly. We reproduce the study that originally made this claim and further investigate what exactly token-based systems learned better compared to sentence-based ones.
  arXiv  Detail & Related papers  (2022-09-29T13:44:28Z)
• The Unreliability of Explanations in Few-Shot In-Context Learning [50.77996380021221]
  We focus on two NLP tasks that involve reasoning over text, namely question answering and natural language inference. We show that explanations judged as good by humans--those that are logically consistent with the input--usually indicate more accurate predictions. We present a framework for calibrating model predictions based on the reliability of the explanations.
  arXiv  Detail & Related papers  (2022-05-06T17:57:58Z)
• Distant finetuning with discourse relations for stance classification [55.131676584455306]
  We propose a new method to extract data with silver labels from raw text to finetune a model for stance classification. We also propose a 3-stage training framework where the noisy level in the data used for finetuning decreases over different stages. Our approach ranks 1st among 26 competing teams in the stance classification track of the NLPCC 2021 shared task Argumentative Text Understanding for AI Debater.
  arXiv  Detail & Related papers  (2022-04-27T04:24:35Z)
• 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)
• Prototypical Representation Learning for Relation Extraction [56.501332067073065]
  This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data. We learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations. Results on several relation learning tasks show that our model significantly outperforms the previous state-of-the-art relational models.
  arXiv  Detail & Related papers  (2021-03-22T08:11:43Z)
• Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision [57.14468881854616]
  We propose an auxiliary training objective that improves the generalization capabilities of neural networks. We use pairs of minimally-different examples with different labels, a.k.a counterfactual or contrasting examples, which provide a signal indicative of the underlying causal structure of the task. Models trained with this technique demonstrate improved performance on out-of-distribution test sets.
  arXiv  Detail & Related papers  (2020-04-20T02:47:49Z)
• Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection [21.02924712220406]
  We build hierarchical explanations by detecting feature interactions. Such explanations visualize how words and phrases are combined at different levels of the hierarchy. Experiments show the effectiveness of the proposed method in providing explanations both faithful to models and interpretable to humans.
  arXiv  Detail & Related papers  (2020-04-04T20:56:37Z)

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.