178 citations · 549 across the 48 of their papers we have counts for
20 papers · 1 filter
Compositional Generalization for Natural Language Interfaces to Web APIs
Saghar Hosseini, Ahmed Hassan Awadallah, Yu Su
This paper presents Okapi, a new dataset for Natural Language to executable web Application Programming Interfaces (NL2API). This dataset is in English and contains 22,508 question…
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding
Subhabrata Mukherjee, Xiaodong Liu, Guoqing Zheng +6
Most recent progress in natural language understanding (NLU) has been driven, in part, by benchmarks such as GLUE, SuperGLUE, SQuAD, etc. In fact, many NLU models have now matched…
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models
Boxin Wang, Chejian Xu, Shuohang Wang +5
Large-scale pre-trained language models have achieved tremendous success across a wide range of natural language understanding (NLU) tasks, even surpassing human performance. Howev…
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models
Xuxi Chen, Tianlong Chen, Weizhu Chen +3
Gigantic pre-trained models have become central to natural language processing (NLP), serving as the starting point for fine-tuning towards a range of downstream tasks. However, tw…
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
Julia Kiseleva, Ziming Li, Mohammad Aliannejadi +12
Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new t…
Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
Yusen Zhang, Ansong Ni, Ziming Mao +6
Text summarization helps readers capture salient information from documents, news, interviews, and meetings. However, most state-of-the-art pretrained language models (LM) are unab…
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding
Mengnan Du, Subhabrata Mukherjee, Yu Cheng +3
Recent work has focused on compressing pre-trained language models (PLMs) like BERT where the major focus has been to improve the in-distribution performance for downstream tasks.…
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization
Ziming Mao, Chen Henry Wu, Ansong Ni +7
Transformer-based models have achieved state-of-the-art performance on short-input summarization. However, they still struggle with summarizing longer text. In this paper, we prese…
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners
Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu +3
We present a new method LiST is short for Lite Prompted Self-Training for parameter-efficient fine-tuning of large pre-trained language models (PLMs) for few-shot learning. LiST im…
A Conditional Generative Matching Model for Multi-lingual Reply Suggestion
Budhaditya Deb, Guoqing Zheng, Milad Shokouhi +1
We study the problem of multilingual automated reply suggestions (RS) model serving many languages simultaneously. Multilingual models are often challenged by model capacity and se…
An Exploratory Study on Long Dialogue Summarization: What Works and What's Next
Yusen Zhang, Ansong Ni, Tao Yu +6
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series. However, real-world dialogues pose a great challeng…
SummerTime: Text Summarization Toolkit for Non-experts
Ansong Ni, Zhangir Azerbayev, Mutethia Mutuma +5
Recent advances in summarization provide models that can generate summaries of higher quality. Such models now exist for a number of summarization tasks, including query-based summ…
MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces
Srinagesh Sharma, Guoqing Zheng, Ahmed Hassan Awadallah
Albeit the universal representational power of pre-trained language models, adapting them onto a specific NLP task still requires a considerably large amount of labeled data. Effec…
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding
Guoqing Zheng, Giannis Karamanolakis, Kai Shu +1
Building machine learning models for natural language understanding (NLU) tasks relies heavily on labeled data. Weak supervision has been proven valuable when large amount of label…
XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation
Subhabrata Mukherjee, Ahmed Hassan Awadallah, Jianfeng Gao
While deep and large pre-trained models are the state-of-the-art for various natural language processing tasks, their huge size poses significant challenges for practical uses in r…
A Dataset and Baselines for Multilingual Reply Suggestion
Mozhi Zhang, Wei Wang, Budhaditya Deb +3
Reply suggestion models help users process emails and chats faster. Previous work only studies English reply suggestion. Instead, we present MRS, a multilingual reply suggestion da…
Fairness via Representation Neutralization
Mengnan Du, Subhabrata Mukherjee, Guanchu Wang +3
Existing bias mitigation methods for DNN models primarily work on learning debiased encoders. This process not only requires a lot of instance-level annotations for sensitive attri…
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning
Mengzhou Xia, Guoqing Zheng, Subhabrata Mukherjee +3
The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-res…
QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization
Ming Zhong, Da Yin, Tao Yu +8
Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may…
Self-Training with Weak Supervision
Giannis Karamanolakis, Subhabrata Mukherjee, Guoqing Zheng +1
State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks. Weak supervision in the form of d…