5.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- SupélecFR485 papers
- University of Applied Sciences and Arts of Southern SwitzerlandCH485 papers
- New York UniversityUS52 papers
- Google (United States)US16 papers
- Princeton UniversityUS15 papers
- Courant Institute of Mathematical SciencesUS13 papers
- Meta (Israel)IL11 papers
- Massachusetts Institute of TechnologyUS10 papers
- Stanford UniversityUS8 papers
- University of California, BerkeleyUS8 papers
- Cornell UniversityUS7 papers
- Carnegie Mellon UniversityUS6 papers
48 papers · 1 filter
MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System
Rhys Compton, Ilya Valmianski, Li Deng +4
We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed…
A Word on Machine Ethics: A Response to Jiang et al. (2021)
Zeerak Talat, Hagen Blix, Josef Valvoda +3
Ethics is one of the longest standing intellectual endeavors of humanity. In recent years, the fields of AI and NLP have attempted to wrangle with how learning systems that interac…
Types of Out-of-Distribution Texts and How to Detect Them
Udit Arora, William Huang, He He
Despite agreement on the importance of detecting out-of-distribution (OOD) examples, there is little consensus on the formal definition of OOD examples and how to best detect them.…
Ontology-Enhanced Slot Filling
Yuhao Ding, Yik-Cheung Tam
Slot filling is a fundamental task in dialog state tracking in task-oriented dialog systems. In multi-domain task-oriented dialog system, user utterances and system responses may m…
A Unified Generative Framework for Aspect-Based Sentiment Analysis
Hang Yan, Junqi Dai, Tuo ji +2
Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms. There exist seven subtasks in ABSA. Most…
A Unified Generative Framework for Various NER Subtasks
Hang Yan, Tao Gui, Junqi Dai +3
Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. Whether the entity spans are nested or discontinuous, the NER task can be cate…
True Few-Shot Learning with Language Models
Ethan Perez, Douwe Kiela, Kyunghyun Cho
Pretrained language models (LMs) perform well on many tasks even when learning from a few examples, but prior work uses many held-out examples to tune various aspects of learning,…
Retrieval Enhanced Model for Commonsense Generation
Han Wang, Yang Liu, Chenguang Zhu +4
Commonsense generation is a challenging task of generating a plausible sentence describing an everyday scenario using provided concepts. Its requirement of reasoning over commonsen…
KLUE: Korean Language Understanding Evaluation
Sungjoon Park, Jihyung Moon, Sungdong Kim +28
We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification,…
Doc2Dict: Information Extraction as Text Generation
Benjamin Townsend, Eamon Ito-Fisher, Lily Zhang +1
Typically, information extraction (IE) requires a pipeline approach: first, a sequence labeling model is trained on manually annotated documents to extract relevant spans; then, wh…
Automatic Romanization of Arabic Bibliographic Records
Eryani Fadhl, Habash Nizar
International library standards require cataloguers to tediously input Romanization of their catalogue records for the benefit of library users without specific language expertise.…
The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models
Go Inoue, Bashar Alhafni, Nurpeiis Baimukan +2
In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained langu…
MixUp Training Leads to Reduced Overfitting and Improved Calibration for the Transformer Architecture
Wancong Zhang, Ieshan Vaidya
MixUp is a computer vision data augmentation technique that uses convex interpolations of input data and their labels to enhance model generalization during training. However, the…
When Do You Need Billions of Words of Pretraining Data?
Yian Zhang, Alex Warstadt, Haau-Sing Li +1
NLP is currently dominated by general-purpose pretrained language models like RoBERTa, which achieve strong performance on NLU tasks through pretraining on billions of words. But w…
NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task
Muhammad Abdul-Mageed, Chiyu Zhang, Houda Bouamor +1
We present the results and findings of the First Nuanced Arabic Dialect Identification Shared Task (NADI). This Shared Task includes two subtasks: country-level dialect identificat…
Meta-Learning for Natural Language Understanding under Continual Learning Framework
Jiacheng Wang, Yong Fan, Duo Jiang +1
Neural network has been recognized with its accomplishments on tackling various natural language understanding (NLU) tasks. Methods have been developed to train a robust model to h…
Rethinking embedding coupling in pre-trained language models
Hyung Won Chung, Thibault Févry, Henry Tsai +2
We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models. We show that decoupled embeddings provi…
An Evaluation Protocol for Generative Conversational Systems
Seolhwa Lee, Heuiseok Lim, João Sedoc
There is a multitude of novel generative models for open-domain conversational systems; however, there is no systematic evaluation of different systems. Systematic comparisons requ…
Six Attributes of Unhealthy Conversation
Ilan Price, Jordan Gifford-Moore, Jory Fleming +6
We present a new dataset of approximately 44000 comments labeled by crowdworkers. Each comment is labelled as either 'healthy' or 'unhealthy', in addition to binary labels for the…
Latent Tree Learning with Ordered Neurons: What Parses Does It Produce?
Yian Zhang
Recent latent tree learning models can learn constituency parsing without any exposure to human-annotated tree structures. One such model is ON-LSTM (Shen et al., 2019), which is t…
Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data
William Huang, Haokun Liu, Samuel R. Bowman
A growing body of work shows that models exploit annotation artifacts to achieve state-of-the-art performance on standard crowdsourced benchmarks---datasets collected from crowdwor…
Precise Task Formalization Matters in Winograd Schema Evaluations
Haokun Liu, William Huang, Dhara A. Mungra +1
Performance on the Winograd Schema Challenge (WSC), a respected English commonsense reasoning benchmark, recently rocketed from chance accuracy to 89% on the SuperGLUE leaderboard,…
Generative Language-Grounded Policy in Vision-and-Language Navigation with Bayes' Rule
Shuhei Kurita, Kyunghyun Cho
Vision-and-language navigation (VLN) is a task in which an agent is embodied in a realistic 3D environment and follows an instruction to reach the goal node. While most of the prev…
Text Generation by Learning from Demonstrations
Richard Yuanzhe Pang, He He
Current approaches to text generation largely rely on autoregressive models and maximum likelihood estimation. This paradigm leads to (i) diverse but low-quality samples due to mis…