Alolika Gon

Yijing Wu, SaiKrishna Rallabandi, Ravisutha Srinivasamurthy et al.

Spoken question answering (SQA) systems are critical for digital assistants and other real-world use cases, but evaluating their performance is a challenge due to the importance of human-spoken questions. This study presents a new large-scale community-shared SQA dataset called HeySQuAD, which includes 76k human-spoken questions, 97k machine-generated questions, and their corresponding textual answers from the SQuAD QA dataset. Our goal is to measure the ability of machines to accurately understand noisy spoken questions and provide reliable answers. Through extensive testing, we demonstrate that training with transcribed human-spoken and original SQuAD questions leads to a significant improvement (12.51%) in answering human-spoken questions compared to training with only the original SQuAD textual questions. Moreover, evaluating with a higher-quality transcription can lead to a further improvement of 2.03%. This research has significant implications for the development of SQA systems and their ability to meet the needs of users in real-world scenarios.

Parag Pravin Dakle, Alolika Gon, Sihan Zha et al.

In this paper, we describe the different approaches explored by the Jetsons team for the Multi-Lingual ESG Impact Duration Inference (ML-ESG-3) shared task. The shared task focuses on predicting the duration and type of the ESG impact of a news article. The shared task dataset consists of 2,059 news titles and articles in English, French, Korean, and Japanese languages. For the impact duration classification task, we fine-tuned XLM-RoBERTa with a custom fine-tuning strategy and using self-training and DeBERTa-v3 using only English translations. These models individually ranked first on the leaderboard for Korean and Japanese and in an ensemble for the English language, respectively. For the impact type classification task, our XLM-RoBERTa model fine-tuned using a custom fine-tuning strategy ranked first for the English language.

Haw-Shiuan Chang, Zonghai Yao, Alolika Gon et al.

Is the output softmax layer, which is adopted by most language models (LMs), always the best way to compute the next word probability? Given so many attention layers in a modern transformer-based LM, are the pointer networks redundant nowadays? In this study, we discover that the answers to both questions are no. This is because the softmax bottleneck sometimes prevents the LMs from predicting the desired distribution and the pointer networks can be used to break the bottleneck efficiently. Based on the finding, we propose several softmax alternatives by simplifying the pointer networks and accelerating the word-by-word rerankers. In GPT-2, our proposals are significantly better and more efficient than mixture of softmax, a state-of-the-art softmax alternative. In summarization experiments, without significantly decreasing its training/testing speed, our best method based on T5-Small improves factCC score by 2 points in CNN/DM and XSUM dataset, and improves MAUVE scores by 30% in BookSum paragraph-level dataset.