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Prasanna Parthasarathi

Alumni

Publications

Local Structure Matters Most: Perturbation Study in NLU
Louis Clouatre
Prasanna Parthasarathi
Amal Zouaq
Sarath Chandar
Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models … (voir plus)are surprisingly insensitive to the order of words. In this paper, we investigate this phenomenon by developing order-altering perturbations on the order of words, subwords, and characters to analyze their effect on neural models' performance on language understanding tasks. We experiment with measuring the impact of perturbations to the local neighborhood of characters and global position of characters in the perturbed texts and observe that perturbation functions found in prior literature only affect the global ordering while the local ordering remains relatively unperturbed. We empirically show that neural models, invariant of their inductive biases, pretraining scheme, or the choice of tokenization, mostly rely on the local structure of text to build understanding and make limited use of the global structure.
2022-04-30
Findings of the Association for Computational Linguistics: ACL 2022 (publié)
doi.org
arxiv.org
Memory Augmented Optimizers for Deep Learning
Paul-Aymeric McRae
Prasanna Parthasarathi
Mahmoud Assran
Sarath Chandar
Popular approaches for minimizing loss in data-driven learning often involve an abstraction or an explicit retention of the history of gradi… (voir plus)ents for efficient parameter updates. The aggregated history of gradients nudges the parameter updates in the right direction even when the gradients at any given step are not informative. Although the history of gradients summarized in meta-parameters or explicitly stored in memory has been shown effective in theory and practice, the question of whether
2022-04-24
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
Local Structure Matters Most in Most Languages
Louis Clouatre
Prasanna Parthasarathi
Amal Zouaq
A. Chandar
2021-12-31
AACL/IJCNLP (2) (publié)
doi.org
arxiv.org
Sometimes We Want Ungrammatical Translations
Prasanna Parthasarathi
Koustuv Sinha
Joelle Pineau
Adina Williams
Rapid progress in Neural Machine Translation (NMT) systems over the last few years has focused primarily on improving translation quality, a… (voir plus)nd as a secondary focus, improving robustness to perturbations (e.g. spelling). While performance and robustness are important objectives, by over-focusing on these, we risk overlooking other important properties. In this paper, we draw attention to the fact that for some applications, faithfulness to the original (input) text is important to preserve, even if it means introducing unusual language patterns in the (output) translation. We propose a simple, novel way to quantify whether an NMT system exhibits robustness or faithfulness, by focusing on the case of word-order perturbations. We explore a suite of functions to perturb the word order of source sentences without deleting or injecting tokens, and measure their effects on the target side. Across several experimental conditions, we observe a strong tendency towards robustness rather than faithfulness. These results allow us to better understand the trade-off between faithfulness and robustness in NMT, and opens up the possibility of developing systems where users have more autonomy and control in selecting which prop-erty is best suited for their use case.
2021-10-31
Empirical Methods in Natural Language Processing (inconnu)
doi.org
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss
Prasanna Parthasarathi
Mohamed Abdelsalam
Joelle Pineau
Sarath Chandar
Neural models trained for next utterance generation in dialogue task learn to mimic the n-gram sequences in the training set with training o… (voir plus)bjectives like negative log-likelihood (NLL) or cross-entropy. Such commonly used training objectives do not foster generating alternate responses to a context. But, the effects of minimizing an alternate training objective that fosters a model to generate alternate response and score it on semantic similarity has not been well studied. We hypothesize that a language generation model can improve on its diversity by learning to generate alternate text during training and minimizing a semantic loss as an auxiliary objective. We explore this idea on two different sized data sets on the task of next utterance generation in goal oriented dialogues. We make two observations (1) minimizing a semantic objective improved diversity in responses in the smaller data set (Frames) but only as-good-as minimizing the NLL in the larger data set (MultiWoZ) (2) large language model embeddings can be more useful as a semantic loss objective than as initialization for token embeddings.
2021-06-30
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue (publié)
doi.org
arxiv.org