Naihao Deng


2023

pdf bib
Query Rewriting for Effective Misinformation Discovery
Ashkan Kazemi | Artem Abzaliev | Naihao Deng | Rui Hou | Scott Hale | Veronica Perez-Rosas | Rada Mihalcea
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Hi-ToM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models
Yufan Wu | Yinghui He | Yilin Jia | Rada Mihalcea | Yulong Chen | Naihao Deng
Findings of the Association for Computational Linguistics: EMNLP 2023

Theory of Mind (ToM) is the ability to reason about one’s own and others’ mental states. ToM plays a critical role in the development of intelligence, language understanding, and cognitive processes. While previous work has primarily focused on first and second-order ToM, we explore higher-order ToM, which involves recursive reasoning on others’ beliefs. %We also incorporate a new deception mechanism in ToM reasoning. We introduce Hi-ToM, a Higher Order Theory of Mind benchmark. Our experimental evaluation using various Large Language Models (LLMs) indicates a decline in performance on higher-order ToM tasks, demonstrating the limitations of current LLMs. We conduct a thorough analysis of different failure cases of LLMs, and share our thoughts on the implications of our findings on the future of NLP.

pdf bib
You Are What You Annotate: Towards Better Models through Annotator Representations
Naihao Deng | Xinliang Zhang | Siyang Liu | Winston Wu | Lu Wang | Rada Mihalcea
Findings of the Association for Computational Linguistics: EMNLP 2023

Annotator disagreement is ubiquitous in natural language processing (NLP) tasks. There are multiple reasons for such disagreements, including the subjectivity of the task, difficult cases, unclear guidelines, and so on. Rather than simply aggregating labels to obtain data annotations, we instead try to directly model the diverse perspectives of the annotators, and explicitly account for annotators’ idiosyncrasies in the modeling process by creating representations for each annotator (*annotator embeddings*) and also their annotations (*annotation embeddings*). In addition, we propose **TID-8**, **T**he **I**nherent **D**isagreement - **8** dataset, a benchmark that consists of eight existing language understanding datasets that have inherent annotator disagreement. We test our approach on TID-8 and show that our approach helps models learn significantly better from disagreements on six different datasets in TID-8 while increasing model size by fewer than 1% parameters. By capturing the unique tendencies and subjectivity of individual annotators through embeddings, our representations prime AI models to be inclusive of diverse viewpoints.

pdf bib
Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond
Siyang Liu | Naihao Deng | Sahand Sabour | Yilin Jia | Minlie Huang | Rada Mihalcea
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

We propose task-adaptive tokenization as a way to adapt the generation pipeline to the specifics of a downstream task and enhance long-form generation in mental health. Inspired by insights from cognitive science, our task-adaptive tokenizer samples variable segmentations from multiple outcomes, with sampling probabilities optimized based on task-specific data. We introduce a strategy for building a specialized vocabulary and introduce a vocabulary merging protocol that allows for the integration of task-specific tokens into the pre-trained model’s tokenization step. Through extensive experiments on psychological question-answering tasks in both Chinese and English, we find that our task-adaptive tokenization approach brings a significant improvement in generation performance while using up to 60% fewer tokens. Preliminary experiments point to promising results when using our tokenization approach with very large language models.

2022

pdf bib
Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect
Naihao Deng | Yulong Chen | Yue Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Text-to-SQL has attracted attention from both the natural language processing and database communities because of its ability to convert the semantics in natural language into SQL queries and its practical application in building natural language interfaces to database systems. The major challenges in text-to-SQL lie in encoding the meaning of natural utterances, decoding to SQL queries, and translating the semantics between these two forms. These challenges have been addressed to different extents by the recent advances. However, there is still a lack of comprehensive surveys for this task. To this end, we review recent progress on text-to-SQL for datasets, methods, and evaluation and provide this systematic survey, addressing the aforementioned challenges and discussing potential future directions. We hope this survey can serve as quick access to existing work and motivate future research.

pdf bib
In-the-Wild Video Question Answering
Santiago Castro | Naihao Deng | Pingxuan Huang | Mihai Burzo | Rada Mihalcea
Proceedings of the 29th International Conference on Computational Linguistics

Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the “in the wild” settings, where the videos are recorded outdoors. We propose WILDQA, a video understanding dataset of videos recorded in outside settings. In addition to video question answering (Video QA), we also introduce the new task of identifying visual support for a given question and answer (Video Evidence Selection). Through evaluations using a wide range of baseline models, we show that WILDQA poses new challenges to the vision and language research communities. The dataset is available at https: //lit.eecs.umich.edu/wildqa/.

pdf bib
Analyzing the Effects of Annotator Gender across NLP Tasks
Laura Biester | Vanita Sharma | Ashkan Kazemi | Naihao Deng | Steven Wilson | Rada Mihalcea
Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022

Recent studies have shown that for subjective annotation tasks, the demographics, lived experiences, and identity of annotators can have a large impact on how items are labeled. We expand on this work, hypothesizing that gender may correlate with differences in annotations for a number of NLP benchmarks, including those that are fairly subjective (e.g., affect in text) and those that are typically considered to be objective (e.g., natural language inference). We develop a robust framework to test for differences in annotation across genders for four benchmark datasets. While our results largely show a lack of statistically significant differences in annotation by males and females for these tasks, the framework can be used to analyze differences in annotation between various other demographic groups in future work. Finally, we note that most datasets are collected without annotator demographics and released only in aggregate form; we call on the community to consider annotator demographics as data is collected, and to release dis-aggregated data to allow for further work analyzing variability among annotators.

pdf bib
The Cross-lingual Conversation Summarization Challenge
Yulong Chen | Ming Zhong | Xuefeng Bai | Naihao Deng | Jing Li | Xianchao Zhu | Yue Zhang
Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges

We propose the shared task of cross-lingual conversation summarization, ConvSumX Challenge, opening new avenues for researchers to investigate solutions that integrate conversation summarization and machine translation. This task can be particularly useful due to the emergence of online meetings and conferences. We use a new benchmark, covering 2 real-world scenarios and 3 language directions, including a low-resource language, for evaluation. We hope that ConvSumX can motivate research to go beyond English and break the barrier for non-English speakers to benefit from recent advances of conversation summarization.

pdf bib
DialogSum Challenge: Results of the Dialogue Summarization Shared Task
Yulong Chen | Naihao Deng | Yang Liu | Yue Zhang
Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges

We report the results of DialogSum Challenge, the shared task on summarizing real-life sce- nario dialogues at INLG 2022. Four teams participate in this shared task and three submit their system reports, exploring different meth- ods to improve the performance of dialogue summarization. Although there is a great im- provement over the baseline models regarding automatic evaluation metrics, such as ROUGE scores, we find that there is a salient gap be- tween model generated outputs and human an- notated summaries by human evaluation from multiple aspects. These findings demonstrate the difficulty of dialogue summarization and suggest that more fine-grained evaluatuion met- rics are in need.