Papers by Garima Lalwani
Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings (2021.findings-emnlp)
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| Challenge: | Recent studies suggest different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces. |
| Approach: | They propose to use Optimal Transport as an alignment objective during fine-tuning to improve multilingual contextualized representations for downstream cross-lingual transfer. |
| Outcome: | The proposed method achieves better performance on two tasks (XNLI and XQuAD) and is competitive with existing methods. |
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models (2020.tacl-1)
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| Challenge: | Recent work shows that pre-trained language models perform poorly on challenging datasets where spurious correlations do not hold. |
| Approach: | They propose to use multi-task learning to improve generalization from minority examples . they propose to combine MTL with auxiliary tasks to improve performance . |
| Outcome: | The proposed model generalizes from minority examples without hurting in-distribution performance. |
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots (D19-1)
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| Challenge: | Prior work on contextual NLU has been limited in terms of the types of contextual signals used and the understanding of their impact on the model. |
| Approach: | They propose a context-aware self-attentive NLU model that uses multiple signals over a variable context window, such as previous intents, slots, dialog acts and utterances, in addition to the current user uttered. |
| Outcome: | The proposed model outperforms a baseline model on two conversational datasets yielding a gain of up to 7% on the IC task. |
Context Analysis for Pre-trained Masked Language Models (2020.findings-emnlp)
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| Challenge: | Pre-trained language models that learn contextualized word representations from a large un-annotated corpus have become a standard component for many downstream NLP tasks. |
| Approach: | They propose to use a masking and gradient approach to evaluate the impact of context on the word representation. |
| Outcome: | The proposed model architectures are architecture agnostic and gradient based. |