448 citations · 800 across the 12 of their papers we have counts for
16 papers
Approximating 1-Wasserstein Distance with Trees
Makoto Yamada, Yuki Takezawa, Ryoma Sato +3
Wasserstein distance, which measures the discrepancy between distributions, shows efficacy in various types of natural language processing (NLP) and computer vision (CV) applicatio…
Improving In-Context Few-Shot Learning via Self-Supervised Training
Mingda Chen, Jingfei Du, Ramakanth Pasunuru +4
Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically adapted specifically for in-context few-shot le…
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
Badr AlKhamissi, Faisal Ladhak, Srini Iyer +7
Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next. It…
Efficient Large Scale Language Modeling with Mixtures of Experts
Mikel Artetxe, Shruti Bhosale, Naman Goyal +21
Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation. This paper presents a detailed empirical study of how autoregressive M…
Few-shot Learning with Multilingual Language Models
Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe +18
Large-scale generative language models such as GPT-3 are competitive few-shot learners. While these models are known to be able to jointly represent many different languages, their…
Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs
Peter Hase, Mona Diab, Asli Celikyilmaz +5
Do language models have beliefs about the world? Dennett (1995) famously argues that even thermostats have beliefs, on the view that a belief is simply an informational state decou…
Fixed Support Tree-Sliced Wasserstein Barycenter
Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva +2
The Wasserstein barycenter has been widely studied in various fields, including natural language processing, and computer vision. However, it requires a high computational cost to…
ProFormer: Towards On-Device LSH Projection Based Transformers
Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva
At the heart of text based neural models lay word representations, which are powerful but occupy a lot of memory making it challenging to deploy to devices with memory constraints…
Environment-agnostic Multitask Learning for Natural Language Grounded Navigation
Xin Eric Wang, Vihan Jain, Eugene Ie +3
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e.g., following natural language instructions or dialog. However, exi…
SemEval-2013 Task 2: Sentiment Analysis in Twitter
Preslav Nakov, Zornitsa Kozareva, Alan Ritter +3
In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hinder…
SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals
Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva +6
In response to the continuing research interest in computational semantic analysis, we have proposed a new task for SemEval-2010: multi-way classification of mutually exclusive sem…
SemEval-2013 Task 4: Free Paraphrases of Noun Compounds
Iris Hendrickx, Preslav Nakov, Stan Szpakowicz +3
In this paper, we describe SemEval-2013 Task 4: the definition, the data, the evaluation and the results. The task is to capture some of the meaning of English noun compounds via p…
On-Device Text Representations Robust To Misspellings via Projections
Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva
Recently, there has been a strong interest in developing natural language applications that live on personal devices such as mobile phones, watches and IoT with the objective to pr…
Transferable Neural Projection Representations
Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva
Neural word representations are at the core of many state-of-the-art natural language processing models. A widely used approach is to pre-train, store and look up word or character…
Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension
Todor Mihaylov, Zornitsa Kozareva, Anette Frank
Reading comprehension is a challenging task in natural language processing and requires a set of skills to be solved. While current approaches focus on solving the task as a whole,…
Variational Reasoning for Question Answering with Knowledge Graph
Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva +2
Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to fu…