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Hits 41 – 60 of 1.101

41
CoDesc: A Large Code–Description Parallel Dataset ...
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42
CausaLM: Causal Model Explanation Through Counterfactual Language Models ...
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43
Beyond Metadata: What Paper Authors Say About Corpora They Use ...
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44
Learning Latent Structures for Cross Action Phrase Relations in Wet Lab Protocols ...
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45
One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers ...
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46
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction ...
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47
The Utility and Interplay of Gazetteers and Entity Segmentation for Named Entity Recognition in English ...
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48
Situation-Based Multiparticipant Chat Summarization: a Concept, an Exploration-Annotation Tool and an Example Collection ...
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49
Don't Let Discourse Confine Your Model: Sequence Perturbations for Improved Event Language Models ...
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50
Boundary Detection with BERT for Span-level Emotion Cause Analysis ...
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51
TexSmart: A System for Enhanced Natural Language Understanding ...
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52
AND does not mean OR: Using Formal Languages to Study Language Models’ Representations ...
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53
Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images ...
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54
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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55
Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search ...
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56
DALC: the Dutch Abusive Language Corpus ...
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57
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition ...
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58
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes ...
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59
Attention-based Contextual Language Model Adaptation for Speech Recognition ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.175 Abstract: Language modeling (LM) for automatic speech recognition (ASR) does not usually incorporate utterance level contextual information. For some domains like voice assistants, however, additional context, such as time at which an utterance was spoken, provides a rich input signal. We introduce an attention mechanism for training neural speech recognition language models on both text and non-linguistic contextual data. When applied to a large dataset of utterances collected by a popular voice assistant platform, our method reduces perplexity by 7.0% relative over a standard LM that does not incorporate contextual information. When evaluated on utterances extracted from the long tail of the dataset, our method improves perplexity by 9.0% relative over a standard LM and by over 2.8% when compared to a state-of-the-art model for contextual LM. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/y2fx-8d09
https://underline.io/lecture/26266-attention-based-contextual-language-model-adaptation-for-speech-recognition
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60
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech ...
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