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Hits 161 – 180 of 1.029

161
Semi-supervised Relation Extraction via Incremental Meta Self-Training ...
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162
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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165
Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context ...
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166
Inducing Transformer’s Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks ...
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167
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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173
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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176
TEET! Tunisian Dataset for Toxic Speech Detection ...
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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 ...
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 ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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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179
We Need to Talk About train-dev-test Splits ...
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180
Learning to Rewrite for Non-Autoregressive Neural Machine Translation ...
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