Akshita Bhagia


On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization
Shruti Palaskar | Akshita Bhagia | Yonatan Bisk | Florian Metze | Alan W Black | Ana Marasovic
Findings of the Association for Computational Linguistics: EMNLP 2022

Combining the visual modality with pretrained language models has been surprisingly effective for simple descriptive tasks such as image captioning. More general text generation however remains elusive. We take a step back and ask: How do these models work for more complex generative tasks, i.e. conditioning on both text and images? Are multimodal models simply visually adapted language models, or do they combine they reason jointly over modalities?We investigate these questions in the context of self-rationalization (jointly generating task labels/answers and free-text explanations) of three tasks: (i) visual question answering in VQA-X, (ii) visual commonsense reasoning in VCR, and (iii) visual-textual entailment in E-SNLI-VE. We show that recent unimodal advances, CLIP image representations and scaling of language models, do not consistently improveself-rationalization in multimodal tasks. We find that no single model type works universally best across tasks, datasets, and finetuning data sizes. Our findings motivate the need for novel general backbones that move text generation from images and text beyond image captioning.

Continued Pretraining for Better Zero- and Few-Shot Promptability
Zhaofeng Wu | Robert L Logan IV | Pete Walsh | Akshita Bhagia | Dirk Groeneveld | Sameer Singh | Iz Beltagy
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Recently introduced language model prompting methods can achieve high accuracy in zero- and few-shot settings while requiring few to no learned task-specific parameters. Nevertheless, these methods still often trail behind full model finetuning. In this work, we investigate if a dedicated continued pretraining stage could improve “promptability”, i.e., zero-shot performance with natural language prompts or few-shot performance with prompt tuning. We reveal settings where existing continued pretraining methods lack promptability. We also identify current methodological gaps, which we fill with thorough large-scale experiments. We demonstrate that a simple recipe, continued pretraining that incorporates a trainable prompt during multi-task learning, leads to improved promptability in both zero- and few-shot settings compared to existing methods, up to 31% relative. On the other hand, we find that continued pretraining using MAML-style meta-learning, a method that directly optimizes few-shot promptability, yields subpar performance. We validate our findings with two prompt tuning methods, and, based on our results, we provide concrete recommendations to optimize promptability for different use cases.

Findings of the WMT’22 Shared Task on Large-Scale Machine Translation Evaluation for African Languages
David Adelani | Md Mahfuz Ibn Alam | Antonios Anastasopoulos | Akshita Bhagia | Marta R. Costa-jussà | Jesse Dodge | Fahim Faisal | Christian Federmann | Natalia Fedorova | Francisco Guzmán | Sergey Koshelev | Jean Maillard | Vukosi Marivate | Jonathan Mbuya | Alexandre Mourachko | Safiyyah Saleem | Holger Schwenk | Guillaume Wenzek
Proceedings of the Seventh Conference on Machine Translation (WMT)

We present the results of the WMT’22 SharedTask on Large-Scale Machine Translation Evaluation for African Languages. The shared taskincluded both a data and a systems track, alongwith additional innovations, such as a focus onAfrican languages and extensive human evaluation of submitted systems. We received 14system submissions from 8 teams, as well as6 data track contributions. We report a largeprogress in the quality of translation for Africanlanguages since the last iteration of this sharedtask: there is an increase of about 7.5 BLEUpoints across 72 language pairs, and the average BLEU scores went from 15.09 to 22.60.