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Does Putting a Linguist in the Loop Improve NLU Data Collection ...
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46 |
Say `YES' to Positivity: Detecting Toxic Language in Workplace Communications ...
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47 |
Unsupervised Multi-View Post-OCR Error Correction With Language Models ...
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48 |
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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49 |
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection ...
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50 |
Multi-granularity Textual Adversarial Attack with Behavior Cloning ...
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51 |
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning ...
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52 |
Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ...
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53 |
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning ...
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54 |
WebSRC: A Dataset for Web-Based Structural Reading Comprehension ...
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55 |
Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning ...
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56 |
Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
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57 |
Adversarial Examples for Evaluating Math Word Problem Solvers ...
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58 |
Pre-train or Annotate? Domain Adaptation with a Constrained Budget ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.409/ Abstract: Recent work has demonstrated that pre-training in-domain language models can boost performance when adapting to a new domain. However, the costs associated with pre-training raise an important question: given a fixed budget, what steps should an NLP practitioner take to maximize performance? In this paper, we study domain adaptation under budget constraints, and approach it as a customer choice problem between data annotation and pre-training. Specifically, we measure the annotation cost of three procedural text datasets and the pre-training cost of three in-domain language models. Then we evaluate the utility of different combinations of pre-training and data annotation under varying budget constraints to assess which combination strategy works best. We find that, for small budgets, spending all funds on annotation leads to the best performance; once the budget becomes large enough, a combination of data annotation and in-domain ...
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Keyword:
Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://underline.io/lecture/37963-pre-train-or-annotatequestion-domain-adaptation-with-a-constrained-budget https://dx.doi.org/10.48448/z1gf-n855
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60 |
Learning with Different Amounts of Annotation: From Zero to Many Labels ...
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