Samson Tan


2022

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You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings
Zeerak Talat | Aurélie Névéol | Stella Biderman | Miruna Clinciu | Manan Dey | Shayne Longpre | Sasha Luccioni | Maraim Masoud | Margaret Mitchell | Dragomir Radev | Shanya Sharma | Arjun Subramonian | Jaesung Tae | Samson Tan | Deepak Tunuguntla | Oskar Van Der Wal
Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models

Evaluating bias, fairness, and social impact in monolingual language models is a difficult task. This challenge is further compounded when language modeling occurs in a multilingual context. Considering the implication of evaluation biases for large multilingual language models, we situate the discussion of bias evaluation within a wider context of social scientific research with computational work.We highlight three dimensions of developing multilingual bias evaluation frameworks: (1) increasing transparency through documentation, (2) expanding targets of bias beyond gender, and (3) addressing cultural differences that exist between languages.We further discuss the power dynamics and consequences of training large language models and recommend that researchers remain cognizant of the ramifications of developing such technologies.

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Interpreting the Robustness of Neural NLP Models to Textual Perturbations
Yunxiang Zhang | Liangming Pan | Samson Tan | Min-Yen Kan
Findings of the Association for Computational Linguistics: ACL 2022

Modern Natural Language Processing (NLP) models are known to be sensitive to input perturbations and their performance can decrease when applied to real-world, noisy data. However, it is still unclear why models are less robust to some perturbations than others. In this work, we test the hypothesis that the extent to which a model is affected by an unseen textual perturbation (robustness) can be explained by the learnability of the perturbation (defined as how well the model learns to identify the perturbation with a small amount of evidence). We further give a causal justification for the learnability metric. We conduct extensive experiments with four prominent NLP models — TextRNN, BERT, RoBERTa and XLNet — over eight types of textual perturbations on three datasets. We show that a model which is better at identifying a perturbation (higher learnability) becomes worse at ignoring such a perturbation at test time (lower robustness), providing empirical support for our hypothesis.

2021

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Reliability Testing for Natural Language Processing Systems
Samson Tan | Shafiq Joty | Kathy Baxter | Araz Taeihagh | Gregory A. Bennett | Min-Yen Kan
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems. Central to these concerns is the question of reliability: Can NLP systems reliably treat different demographics fairly and function correctly in diverse and noisy environments? To address this, we argue for the need for reliability testing and contextualize it among existing work on improving accountability. We show how adversarial attacks can be reframed for this goal, via a framework for developing reliability tests. We argue that reliability testing — with an emphasis on interdisciplinary collaboration — will enable rigorous and targeted testing, and aid in the enactment and enforcement of industry standards.

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Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots
Samson Tan | Shafiq Joty
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching

Multilingual models have demonstrated impressive cross-lingual transfer performance. However, test sets like XNLI are monolingual at the example level. In multilingual communities, it is common for polyglots to code-mix when conversing with each other. Inspired by this phenomenon, we present two strong black-box adversarial attacks (one word-level, one phrase-level) for multilingual models that push their ability to handle code-mixed sentences to the limit. The former (PolyGloss) uses bilingual dictionaries to propose perturbations and translations of the clean example for sense disambiguation. The latter (Bumblebee) directly aligns the clean example with its translations before extracting phrases as perturbations. Bumblebee has a success rate of 89.75% against XLM-R-large, bringing its average accuracy of 79.85 down to 8.18 on XNLI. Finally, we propose an efficient adversarial training scheme, Code-mixed Adversarial Training (CAT), that trains in the same number of steps as the original model. Even after controlling for the extra training data introduced, CAT improves model accuracy when the model is prevented from relying on lexical overlaps (+3.45), with a negligible drop (-0.15 points) in performance on the original XNLI test set. t-SNE visualizations reveal that CAT improves a model’s language agnosticity. This paper will be published in the proceedings of NAACL-HLT 2021.

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Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots
Samson Tan | Shafiq Joty
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Multilingual models have demonstrated impressive cross-lingual transfer performance. However, test sets like XNLI are monolingual at the example level. In multilingual communities, it is common for polyglots to code-mix when conversing with each other. Inspired by this phenomenon, we present two strong black-box adversarial attacks (one word-level, one phrase-level) for multilingual models that push their ability to handle code-mixed sentences to the limit. The former uses bilingual dictionaries to propose perturbations and translations of the clean example for sense disambiguation. The latter directly aligns the clean example with its translations before extracting phrases as perturbations. Our phrase-level attack has a success rate of 89.75% against XLM-R-large, bringing its average accuracy of 79.85 down to 8.18 on XNLI. Finally, we propose an efficient adversarial training scheme that trains in the same number of steps as the original model and show that it creates more language-invariant representations, improving clean and robust accuracy in the absence of lexical overlap without degrading performance on the original examples.

2020

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Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding
Samson Tan | Shafiq Joty | Lav Varshney | Min-Yen Kan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English and African American Vernacular English. Although comprehension by human readers is usually unimpaired by non-standard inflections, current NLP systems are not yet robust. We propose Base-Inflection Encoding (BITE), a method to tokenize English text by reducing inflected words to their base forms before reinjecting the grammatical information as special symbols. Fine-tuning pretrained NLP models for downstream tasks using our encoding defends against inflectional adversaries while maintaining performance on clean data. Models using BITE generalize better to dialects with non-standard inflections without explicit training and translation models converge faster when trained with BITE. Finally, we show that our encoding improves the vocabulary efficiency of popular data-driven subword tokenizers. Since there has been no prior work on quantitatively evaluating vocabulary efficiency, we propose metrics to do so.

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It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations
Samson Tan | Shafiq Joty | Min-Yen Kan | Richard Socher
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, etc.). We perturb the inflectional morphology of words to craft plausible and semantically similar adversarial examples that expose these biases in popular NLP models, e.g., BERT and Transformer, and show that adversarially fine-tuning them for a single epoch significantly improves robustness without sacrificing performance on clean data.