Yilun Zhao, Yitao Long, Hongjun Liu, Ryo Kamoi, Linyong Nan, Lyuhao Chen, Yixin Liu, Xiangru Tang, Rui Zhang, Arman Cohan. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Maciej Kryscinski, Semih Yavuz, Ye Liu, Xi Victoria Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Yixin Liu, Alex Fabbri, Pengfei Liu, Yilun Zhao, Linyong Nan, Ruilin Han, Simeng Han, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, Dragomir Radev. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Yilun Zhao, Chen Zhao, Linyong Nan, Zhenting Qi, Wenlin Zhang, Xiangru Tang, Boyu Mi, Dragomir Radev. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Logical Table -to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables.There currently exists two challenges in the field: 1) Faithfulness: how to generate sentences that are factually correct given the table content; 2) Diversity: how to generate multiple sentences that offer different perspectives on the table.This work proposes LOFT, which utilizes logic forms as fact verifiers and content planners to control LT2T generation.Experimental results on the LOGICNLG dataset demonstrate that LOFT is the first model that addresses unfaithfulness and lack of diversity issues simultaneously.Our code is publicly available at https: //github.com/Yale-LILY/LoFT.

Yilun Zhao, Zhenting Qi, Linyong Nan, Boyu Mi, Yixin Liu, Weijin Zou, Simeng Han, Ruizhe Chen, Xiangru Tang, Yumo Xu, Dragomir Radev, Arman Cohan. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

Neural attention models have achieved significant improvements on many natural language processing tasks.However, the quadratic memory complexity of the self-attention module with respect to the input length hinders their applications in long text summarization.Instead of designing more efficient attention modules, we approach this problem by investigating if models with a restricted context can have competitive performance compared with the memory-efficient attention models that maintain a global context by treating the input as a single sequence.Our model is applied to individual pages, which contain parts of inputs grouped by the principle of locality, during both the encoding and decoding stages.We empirically investigated three kinds of locality in text summarization at different levels of granularity, ranging from sentences to documents.Our experimental results show that our model has a better performance compared with strong baseline models with efficient attention modules, and our analysis provides further insights into our locality-aware modeling strategy.1

Unfaithful text generation is a common problem for text generation systems.In the case of Data-to-Text (D2T) systems, the factuality of the generated text is particularly crucial for any real-world applications.We introduce R2D2, a training framework that addresses unfaithful Data-to-Text generation by training a system both as a generator and a faithfulness discriminator with additional replacement detection and unlikelihood learning tasks.To facilitate such training, we propose two methods for sampling unfaithful sentences.We argue that the poor entity retrieval capability of D2T systems is one of the primary sources of unfaithfulness, so in addition to the existing metrics, we further propose named entity based metrics to evaluate the fidelity of D2T generations.Our experimental results show that R2D2 systems could effectively mitigate the unfaithful text generation, and they achieve new state-of-the-art results on FeTaQA, LogicNLG, and ToTTo, all with significant improvements.

Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills.Current models with tablespecific architectures and pre-training methods perform well on understanding table structures, but they still struggle with tasks that require various table reasoning skills.In this work, we develop REASTAP to show that high-level table reasoning skills can be injected into models during pre-training without a complex tablespecific architecture design.We define 7 table reasoning skills, such as numerical operation, temporal comparison, and conjunction.Each reasoning skill is associated with one example generator, which synthesizes questions over semi-structured tables according to the sampled templates.We model the table pre-training task as a sequence generation task and pretrain REASTAP to generate precise answers to the synthetic examples.REASTAP is evaluated on four benchmarks covering three downstream tasks including: 1) WIKISQL-WEAK and WIKITQ for Table Question Answering; 2) TABFACT for Table Fact Verification; and 3) LOGICNLG for Faithful Table-to-Text Generation.Experimental results demonstrate that REASTAP achieves new state-of-the-art performance on all benchmarks and delivers a significant improvement on low-resource setting.

Abstract Existing table question answering datasets contain abundant factual questions that primarily evaluate a QA system’s comprehension of query and tabular data. However, restricted by their short-form answers, these datasets fail to include question–answer interactions that represent more advanced and naturally occurring information needs: questions that ask for reasoning and integration of information pieces retrieved from a structured knowledge source. To complement the existing datasets and to reveal the challenging nature of the table-based question answering task, we introduce FeTaQA, a new dataset with 10K Wikipedia-based {table, question, free-form answer, supporting table cells} pairs. FeTaQA is collected from noteworthy descriptions of Wikipedia tables that contain information people tend to seek; generation of these descriptions requires advanced processing that humans perform on a daily basis: Understand the question and table, retrieve, integrate, infer, and conduct text planning and surface realization to generate an answer. We provide two benchmark methods for the proposed task: a pipeline method based on semantic parsing-based QA systems and an end-to-end method based on large pretrained text generation models, and show that FeTaQA poses a challenge for both methods.

Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

We release an urgency dataset that consists of English tweets relating to natural crises.The set is annotated along with annotations of their corresponding urgency status.Additionally, we release evaluation datasets for two low-resource languages, i.e.Sinhala and Odia, and demonstrate an effective zero-shot transfer from English to these two languages by training cross-lingual classifiers.We adopt cross-lingual embeddings constructed using different methods to extract features of the tweets, including a few state-of-the-art contextual embeddings such as BERT, RoBERTa and XLM-R.We train a variety of classifier architectures, supervised and semi supervised, on the extracted features.We also further experiment with ensembling the various classifiers.With very limited amounts of labeled data in English and zero data in the low resource languages, we show a successful framework of training monolingual and cross-lingual classifiers using deep learning methods which are known to be data hungry.Specifically, we show that the recent deep contextual embeddings are also helpful when dealing with very small-scale datasets.Classifiers that incorporate RoBERTa yield the best performance for the English urgency detection task, with 25% F1 score absolute improvement over the baselines.For the zero-shot transfer to low resource languages, classifiers that use LASER features perform the best for Sinhala transfer while XLM-R features benefit the Odia transfer the most.