Claire Cardie

Improving Agreement and Disagreement Identification in Online Discussions with A Socially-Tuned Sentiment Lexicon

2016 · arXiv (Cornell University)

We study the problem of agreement and disagreement detection in online discussions. An isotonic Conditional Random Fields (isotonic CRF) based sequential model is proposed to make predictions on sentence- or segment-level. We automatically construct a …

Keeping Notes: Conditional Natural Language Generation with a Scratchpad Encoder

2019

We introduce the Scratchpad Mechanism, a novel addition to the sequence-to-sequence (seq2seq) neural network architecture and demonstrate its effectiveness in improving the overall fluency of seq2seq models for natural language generation tasks. By enabling the …

Multi-Source Cross-Lingual Model Transfer: Learning What to Share

2019

Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated training data for various NLP …

Argument Mining with Structured SVMs and RNNs

2017 · arXiv (Cornell University)

We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% …

GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction

2021

We revisit the classic problem of documentlevel role-filler entity extraction (REE) for template filling. We argue that sentence-level approaches are ill-suited to the task and introduce a generative transformer-based encoderdecoder framework (GRIT) that is designed …

SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability

2015

Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Iñigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, Janyce Wiebe. Proceedings of the 9th International Workshop on Semantic Evaluation …

Learning to Ask: Neural Question Generation for Reading Comprehension

2017 · arXiv (Cornell University)

We study automatic question generation for sentences from text passages in reading comprehension. We introduce an attention-based sequence learning model for the task and investigate the effect of encoding sentence- vs. paragraph-level information. In contrast …

Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees

2017

We present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract semantic relations between entity mentions …

Unsupervised Multilingual Word Embeddings

2018

Multilingual Word Embeddings (MWEs) represent words from multiple languages in a single distributional vector space. Unsupervised MWE (UMWE) methods acquire multilingual embeddings without cross-lingual supervision, which is a significant advantage over traditional supervised approaches and …

DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension

2019 · Transactions of the Association for Computational Linguistics

We present DREAM, the first dialogue-based multiple-choice reading comprehension data set. Collected from English as a Foreign Language examinations designed by human experts to evaluate the comprehension level of Chinese learners of English, our data …

Harvesting Paragraph-level Question-Answer Pairs from Wikipedia

2018

We study the task of generating from Wikipedia articles question-answer pairs that cover content beyond a single sentence. We propose a neural network approach that incorporates coreference knowledge via a novel gating mechanism. Compared to …

Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

2018 · Transactions of the Association for Computational Linguistics

In recent years great success has been achieved in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most languages do not enjoy such an abundance of labeled data. …

Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding

2020

Few works in the literature of event extraction have gone beyond individual sentences to make extraction decisions. This is problematic when the information needed to recognize an event argument is spread across multiple sentences. We …

Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings

2020

Contextualized representations (e.g. ELMo, BERT) have become the default pretrained representations for downstream NLP applications. In some settings, this transition has rendered their static embedding predecessors (e.g. Word2Vec, GloVe) obsolete. As a side-effect, we observe …

Event Extraction by Answering (Almost) Natural Questions

2020

The problem of event extraction requires detecting the event trigger and extracting its corresponding arguments.