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Negative language transfer in learner English: A new dataset ...
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Parallel sentences mining with transfer learning in an unsupervised setting ...
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Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation ...
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Detoxifying Language Models Risks Marginalizing Minority Voices ...
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Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding ...
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Knowledge Enhanced Masked Language Model for Stance Detection ...
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LIVE SESSION: 15D-Oral: Phonology, Morphology and Word Segmentation ...
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How low is too low? A monolingual take on lemmatisation in Indian languages ...
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Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model ...
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MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories ...
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A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models ...
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DirectProbe: Studying Representations without Classifiers ...
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Challenging distributional models with a conceptual network of philosophical terms ...
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ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding ...
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Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems ...
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CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems ...
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multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning ...
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Abstract:
Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.287/ Abstract: We focus on a type of linguistic formal reasoning where the goal is to reason over explicit knowledge in the form of natural language facts and rules (Clark et al. 2020). A recent work, named PRover (Saha et al. 2020), performs such reasoning by answering a question and also generating a proof graph that explains the answer. However, compositional reasoning is not always unique and there may be multiple ways of reaching the correct answer. Thus, in our work, we address a new and challenging problem of generating multiple proof graphs for reasoning over natural language rule-bases. Each proof provides a different rationale for the answer, thereby improving the interpretability of such reasoning systems. In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof ...
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Keyword:
Artificial Intelligence; Computer Science and Engineering; Intelligent System; Natural Language Processing
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URL: https://underline.io/lecture/19592-multiprover-generating-multiple-proofs-for-improved-interpretability-in-rule-reasoning https://dx.doi.org/10.48448/c20y-hz48
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