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Semi-supervised Relation Extraction via Incremental Meta Self-Training ...
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Data and Parameter Scaling Laws for Neural Machine Translation ...
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163 |
Exploring Multitask Learning for Low-Resource Abstractive Summarization ...
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164 |
Investigating Numeracy of a Text-to-Text Transfer model ...
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Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context ...
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Inducing Transformer’s Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks ...
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Exophoric Pronoun Resolution in Dialogues with Topic Regularization ...
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168 |
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning ...
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169 |
Idiosyncratic but not Arbitrary: Learning Idiolects in Online Registers Reveals Distinctive yet Consistent Individual Styles ...
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170 |
On the Cross-lingual Transferability of Contextualized Sense Embeddings ...
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171 |
CATE: A Contrastive Pre-trained Model for Metaphor Detection with Semi-supervised Learning ...
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172 |
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders ...
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Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints ...
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174 |
Universal Simultaneous Machine Translation with Mixture-of-Experts Wait-k Policy ...
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175 |
Understanding Guided Image Captioning Performance across Domains ...
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177 |
Biomedical Concept Normalization by Leveraging Hypernyms ...
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178 |
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.588/ Abstract: One of the challenges faced by conversational agents is their inability to identify unstated presumptions of their users’ commands, a task trivial for humans due to their common sense. In this paper, we propose a zeroshot commonsense reasoning system for conversational agents in an attempt to achieve this. Our reasoner uncovers unstated presumptions from user commands satisfying a general template of if-(state ), then-(action ), because-(goal ). Our reasoner uses a state-ofthe-art transformer-based generative commonsense knowledge base (KB) as its source of background knowledge for reasoning. We propose a novel and iterative knowledge query mechanism to extract multi-hop reasoning chains from the neural KB which uses symbolic logic rules to significantly reduce the search space. Similar to any KBs gathered to date, our commonsense KB is prone to missing knowledge. Therefore, we propose to conversationally elicit the missing ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/sd02-sm43 https://underline.io/lecture/37394-conversational-multi-hop-reasoning-with-neural-commonsense-knowledge-and-symbolic-logic-rules
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Learning to Rewrite for Non-Autoregressive Neural Machine Translation ...
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