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TexSmart: A System for Enhanced Natural Language Understanding ...
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62
AND does not mean OR: Using Formal Languages to Study Language Models’ Representations ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.21 Abstract: A current open question in natural language processing is to what extent language models, which are trained with access only to the form of language, are able to capture the meaning of language. This question is challenging to answer in general, as there is no clear line between meaning and form, but rather meaning constrains form in consistent ways. The goal of this study is to offer insights into a narrower but critical subquestion: Under what conditions should we expect that meaning and form covary sufficiently, such that a language model with access only to form might nonetheless succeed in emulating meaning? Focusing on several formal languages (propositional logic and a set of programming languages), we generate training corpora using a variety of motivated constraints, and measure a distributional language model's ability to differentiate logical symbols (AND, OR, and NOT). Our findings are largely negative: none of our simulated ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25953-and-does-not-mean-or-using-formal-languages-to-study-language-models
https://dx.doi.org/10.48448/x804-3938
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63
Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images ...
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64
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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65
Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search ...
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66
DALC: the Dutch Abusive Language Corpus ...
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67
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition ...
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68
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes ...
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69
Attention-based Contextual Language Model Adaptation for Speech Recognition ...
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70
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech ...
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71
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning ...
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72
LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification ...
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73
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions ...
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74
Evidence-based Factual Error Correction ...
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75
SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images ...
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76
A DQN-based Approach to Finding Precise Evidences for Fact Verification ...
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77
Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event ...
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78
A Span-based Dynamic Local Attention Model for Sequential Sentence Classification ...
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79
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection ...
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80
2A: Sentiment Analysis, Stylistic Analysis, and Argument Mining #1 ...
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