Search Results for author:
Found 22 papers, 5 papers with code
Label Semantics for Few Shot Named Entity Recognition
We study the problem of few shot learning for named entity recognition.
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
Detecting what emotions are expressed in text is a well-studied problem in natural language processing.
Meta learning to classify intent and slot labels with noisy few shot examples
Recently deep learning has dominated many machine learning areas, including spoken language understanding (SLU).
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging
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 representations that can then be further fine-tuned for downstream tasks to much success.
Resource-Enhanced Neural Model for Event Argument Extraction
(2) Capturing the long-range dependency, specifically, the connection between an event trigger and a distant event argument.
Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions
To measure the true progress of existing models, we split the test set into two sets, one which requires reasoning on linguistic structure and the other which doesn't.
Exploring Content Selection in Summarization of Novel Chapters
We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides.
Evaluating Robustness to Input Perturbations for Neural Machine Translation
Neural Machine Translation (NMT) models are sensitive to small perturbations in the input.
Joint translation and unit conversion for end-to-end localization
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.
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations.
Robustness to Capitalization Errors in Named Entity Recognition
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 noise.
Neural Word Decomposition Models for Abusive Language Detection
User generated text on social media often suffers from a lot of undesired characteristics including hatespeech, abusive language, insults etc.
Span-Level Model for Relation Extraction
Relation Extraction is the task of identifying entity mention spans in raw text and then identifying relations between pairs of the entity mentions.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
Training Neural Machine Translation To Apply Terminology Constraints
This paper proposes a novel method to inject custom terminology into neural machine translation at run time.
AMR Parsing using Stack-LSTMs
We present a transition-based AMR parser that directly generates AMR parses from plain text.
Ranked #11 on
AMR Parsing
on LDC2014T12
Attention-based Vocabulary Selection for NMT Decoding
Usually, the candidate lists are a combination of external word-to-word aligner, phrase table entries or most frequent words.
Ensemble Distillation for Neural Machine Translation
Knowledge distillation describes a method for training a student network to perform better by learning from a stronger teacher network.
Beam Search Strategies for Neural Machine Translation
In this paper, we concentrate on speeding up the decoder by applying a more flexible beam search strategy whose candidate size may vary at each time step depending on the candidate scores.
Fast Domain Adaptation for Neural Machine Translation
The basic concept in NMT is to train a large Neural Network that maximizes the translation performance on a given parallel corpus.
Temporal Attention Model for Neural Machine Translation
Attention-based Neural Machine Translation (NMT) models suffer from attention deficiency issues as has been observed in recent research.
Zero-Resource Translation with Multi-Lingual Neural Machine Translation
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.
