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05/26/2023
Characterizing and Measuring Linguistic Dataset Drift
NLP models often degrade in performance when real world data distributio...
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05/22/2023
Taxonomy Expansion for Named Entity Recognition
Training a Named Entity Recognition (NER) model often involves fixing a ...
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05/19/2023
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment
We explore how weak supervision on abundant unlabeled data can be levera...
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05/18/2023
Comparing Biases and the Impact of Multilingual Training across Multiple Languages
Studies in bias and fairness in natural language processing have primari...
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03/21/2023
Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization
Opinion summarization provides an important solution for summarizing opi...
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02/23/2023
Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views
Temporal concept drift refers to the problem of data changing over time....
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11/09/2022
Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection
Summarizing novel chapters is a difficult task due to the input length a...
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10/12/2022
Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analy...
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10/11/2022
Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents
We investigate semi-structured document classification in a zero-shot se...
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04/23/2022
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning
Recent work has found that multi-task training with a large number of di...
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03/16/2022
Label Semantics for Few Shot Named Entity Recognition
We study the problem of few shot learning for named entity recognition. ...
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09/16/2021
A Bag of Tricks for Dialogue Summarization
Dialogue summarization comes with its own peculiar challenges as opposed...
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09/07/2021
How much pretraining data do language models need to learn syntax?
Transformers-based pretrained language models achieve outstanding result...
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04/17/2021
Sequential Cross-Document Coreference Resolution
Relating entities and events in text is a key component of natural langu...
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01/27/2021
On the Evolution of Syntactic Information Encoded by BERT's Contextualized Representations
The adaptation of pretrained language models to solve supervised tasks h...
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01/26/2021
Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings
We propose a method for online news stream clustering that is a variant ...
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10/27/2020
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 archit...
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10/21/2020
Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas
In entity linking, mentions of named entities in raw text are disambigua...
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10/20/2020
Transition-based Parsing with Stack-Transformers
Modeling the parser state is key to good performance in transition-based...
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10/12/2020
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models
Humans can learn structural properties about a word from minimal experie...
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10/06/2020
Resource-Enhanced Neural Model for Event Argument Extraction
Event argument extraction (EAE) aims to identify the arguments of an eve...
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04/08/2020
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 me...
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01/22/2020
Transition-Based Dependency Parsing using Perceptron Learner
Syntactic parsing using dependency structures has become a standard tech...
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05/31/2019
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Our work involves enriching the Stack-LSTM transition-based AMR parser (...
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03/08/2019
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
We deploy the methods of controlled psycholinguistic experimentation to ...
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03/03/2019
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
State-of-the-art LSTM language models trained on large corpora learn seq...
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02/26/2019
Recursive Subtree Composition in LSTM-Based Dependency Parsing
The need for tree structure modelling on top of sequence modelling is an...
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06/08/2018
Multilingual Neural Machine Translation with Task-Specific Attention
Multilingual machine translation addresses the task of translating betwe...
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04/24/2018
Scheduled Multi-Task Learning: From Syntax to Translation
Neural encoder-decoder models of machine translation have achieved impre...
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04/13/2018
Pieces of Eight: 8-bit Neural Machine Translation
Neural machine translation has achieved levels of fluency and adequacy t...
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03/06/2018
Multimodal Emoji Prediction
Emojis are small images that are commonly included in social media text ...
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09/01/2017
Arc-Standard Spinal Parsing with Stack-LSTMs
We present a neural transition-based parser for spinal trees, a dependen...
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07/24/2017
AMR Parsing using Stack-LSTMs
We present a transition-based AMR parser that directly generates AMR par...
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02/23/2017
Are Emojis Predictable?
Emojis are ideograms which are naturally combined with plain text to vis...
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01/15/2017
DyNet: The Dynamic Neural Network Toolkit
We describe DyNet, a toolkit for implementing neural network models base...
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11/17/2016
What Do Recurrent Neural Network Grammars Learn About Syntax?
Recurrent neural network grammars (RNNG) are a recently proposed probabi...
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06/29/2016
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
We present a transition-based parser that jointly produces syntactic and...
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03/21/2016
Static and Dynamic Feature Selection in Morphosyntactic Analyzers
We study the use of greedy feature selection methods for morphosyntactic...
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03/11/2016
Training with Exploration Improves a Greedy Stack-LSTM Parser
We adapt the greedy Stack-LSTM dependency parser of Dyer et al. (2015) t...
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03/04/2016
Neural Architectures for Named Entity Recognition
State-of-the-art named entity recognition systems rely heavily on hand-c...
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02/25/2016
Recurrent Neural Network Grammars
We introduce recurrent neural network grammars, probabilistic models of ...
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02/04/2016
Many Languages, One Parser
We train one multilingual model for dependency parsing and use it to par...
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08/04/2015
Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs
We present extensions to a continuous-state dependency parsing method th...
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05/29/2015
