1 |
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Specializing Multilingual Language Models: An Empirical Study ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Provable Limitations of Acquiring Meaning from Ungrounded Form: What will Future Language Models Understand? ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Measuring Association Between Labels and Free-Text Rationales ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Promoting Graph Awareness in Linearized Graph-to-Text Generation ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Challenges in Automated Debiasing for Toxic Language Detection ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Competency Problems: On Finding and Removing Artifacts in Language Data ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Semantic Comparisons for Natural Language Processing Applications
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Challenges in Automated Debiasing for Toxic Language Detection
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
The Multilingual Amazon Reviews Corpus ...
|
|
|
|
Abstract:
We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID, and the coarse-grained product category (e.g., 'books', 'appliances', etc.) The corpus is balanced across the 5 possible star ratings, so each rating constitutes 20% of the reviews in each language. For each language, there are 200,000, 5,000, and 5,000 reviews in the training, development, and test sets, respectively. We report baseline results for supervised text classification and zero-shot cross-lingual transfer learning by fine-tuning a multilingual BERT model on reviews data. We propose the use of mean absolute error (MAE) instead of classification accuracy for this task, since MAE accounts for the ... : To appear in EMNLP 2020 ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Information Retrieval cs.IR; Machine Learning cs.LG
|
|
URL: https://dx.doi.org/10.48550/arxiv.2010.02573 https://arxiv.org/abs/2010.02573
|
|
BASE
|
|
Hide details
|
|
18 |
Unsupervised Bitext Mining and Translation via Self-trained Contextual Embeddings ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Evaluating Models' Local Decision Boundaries via Contrast Sets ...
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Grounded Compositional Outputs for Adaptive Language Modeling ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|