284 citations · 603 across the 13 of their papers we have counts for
20 papers
Label Semantics for Few Shot Named Entity Recognition
Jie Ma, Miguel Ballesteros, Srikanth Doss +4
We study the problem of few shot learning for named entity recognition. Specifically, we leverage the semantic information in the names of the labels as a way of giving the model a…
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
Elsbeth Turcan, Shuai Wang, Rishita Anubhai +3
Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer grained emotion analysis such as what causes an e…
Meta learning to classify intent and slot labels with noisy few shot examples
Shang-Wen Li, Jason Krone, Shuyan Dong +2
Recently deep learning has dominated many machine learning areas, including spoken language understanding (SLU). However, deep learning models are notorious for being data-hungry,…
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging
Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai +4
Leveraging large amounts of unlabeled data using Transformer-like architectures, like BERT, has gained popularity in recent times owing to their effectiveness in learning general r…
Resource-Enhanced Neural Model for Event Argument Extraction
Jie Ma, Shuai Wang, Rishita Anubhai +2
Event argument extraction (EAE) aims to identify the arguments of an event and classify the roles that those arguments play. Despite great efforts made in prior work, there remain…
Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions
Arjun R Akula, Spandana Gella, Yaser Al-Onaizan +2
Visual referring expression recognition is a challenging task that requires natural language understanding in the context of an image. We critically examine RefCOCOg, a standard be…
Exploring Content Selection in Summarization of Novel Chapters
Faisal Ladhak, Bryan Li, Yaser Al-Onaizan +1
We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides. This is a harder task than the news summarization…
Evaluating Robustness to Input Perturbations for Neural Machine Translation
Xing Niu, Prashant Mathur, Georgiana Dinu +1
Neural Machine Translation (NMT) models are sensitive to small perturbations in the input. Robustness to such perturbations is typically measured using translation quality metrics…
Joint translation and unit conversion for end-to-end localization
Georgiana Dinu, Prashant Mathur, Marcello Federico +2
A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions. In this paper…
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
Miguel Ballesteros, Rishita Anubhai, Shuai Wang +6
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events wit…
Robustness to Capitalization Errors in Named Entity Recognition
Sravan Bodapati, Hyokun Yun, Yaser Al-Onaizan
Robustness to capitalization errors is a highly desirable characteristic of named entity recognizers, yet we find standard models for the task are surprisingly brittle to such nois…
Neural Word Decomposition Models for Abusive Language Detection
Sravan Babu Bodapati, Spandana Gella, Kasturi Bhattacharjee +1
User generated text on social media often suffers from a lot of undesired characteristics including hatespeech, abusive language, insults etc. that are targeted to attack or abuse…
Training Neural Machine Translation To Apply Terminology Constraints
Georgiana Dinu, Prashant Mathur, Marcello Federico +1
This paper proposes a novel method to inject custom terminology into neural machine translation at run time. Previous works have mainly proposed modifications to the decoding algor…
AMR Parsing using Stack-LSTMs
Miguel Ballesteros, Yaser Al-Onaizan
We present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our ex…
Attention-based Vocabulary Selection for NMT Decoding
Baskaran Sankaran, Markus Freitag, Yaser Al-Onaizan
Neural Machine Translation (NMT) models usually use large target vocabulary sizes to capture most of the words in the target language. The vocabulary size is a big factor when deco…
Beam Search Strategies for Neural Machine Translation
Markus Freitag, Yaser Al-Onaizan
The basic concept in Neural Machine Translation (NMT) is to train a large Neural Network that maximizes the translation performance on a given parallel corpus. NMT is then using a…
Ensemble Distillation for Neural Machine Translation
Markus Freitag, Yaser Al-Onaizan, Baskaran Sankaran
Knowledge distillation describes a method for training a student network to perform better by learning from a stronger teacher network. Translating a sentence with an Neural Machin…
Fast Domain Adaptation for Neural Machine Translation
Markus Freitag, Yaser Al-Onaizan
Neural Machine Translation (NMT) is a new approach for automatic translation of text from one human language into another. The basic concept in NMT is to train a large Neural Netwo…
Temporal Attention Model for Neural Machine Translation
Baskaran Sankaran, Haitao Mi, Yaser Al-Onaizan +1
Attention-based Neural Machine Translation (NMT) models suffer from attention deficiency issues as has been observed in recent research. We propose a novel mechanism to address som…
Zero-Resource Translation with Multi-Lingual Neural Machine Translation
Orhan Firat, Baskaran Sankaran, Yaser Al-Onaizan +2
In this paper, we propose a novel finetuning algorithm for the recently introduced multi-way, mulitlingual neural machine translate that enables zero-resource machine translation.…