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Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent ...
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Softmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large ...
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GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.35/ Abstract: Practical dialogue systems require robust methods of detecting out-of-scope (OOS) utterances to avoid conversational breakdowns and related failure modes. Directly training a model with labeled OOS examples yields reasonable performance, but obtaining such data is a resource-intensive process. To tackle this limited-data problem, previous methods focus on better modeling the distribution of in-scope (INS) examples. We introduce GOLD as an orthogonal technique that augments existing data to train better OOS detectors operating in low-data regimes. GOLD generates pseudo-labeled candidates using samples from an auxiliary dataset and keeps only the most beneficial candidates for training through a novel filtering mechanism. In experiments across three target benchmarks, the top GOLD model outperforms all existing methods on all key metrics: AUROC, AUPR, FPR@N. We also analyze the unique properties of OOS data to identify key factors for ...
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Keyword:
Language Models; Natural Language Processing; Semantic Evaluation; Sociolinguistics
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URL: https://dx.doi.org/10.48448/00ff-w640 https://underline.io/lecture/37387-gold-improving-out-of-scope-detection-in-dialogues-using-data-augmentation
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RuleBERT: Teaching Soft Rules to Pre-Trained Language Models ...
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Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models ...
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On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings ...
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Enhanced Language Representation with Label Knowledge for Span Extraction ...
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The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers ...
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VeeAlign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment ...
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Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning ...
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On Classifying whether Two Texts are on the Same Side of an Argument ...
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Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP ...
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MTAdam: Automatic Balancing of Multiple Training Loss Terms ...
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Types of Out-of-Distribution Texts and How to Detect Them ...
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Asking It All: Generating Contextualized Questions for any Semantic Role ...
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Competency Problems: On Finding and Removing Artifacts in Language Data ...
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