Search Results for author:
Found 61 papers, 31 papers with code
Named Entity Recognition in Tweets: An Experimental Study
The performance of standard NLP tools is severely degraded on tweets.
Modeling Missing Data in Distant Supervision for Information Extraction
Distant supervision algorithms learn information extraction models given only large readily available databases and text collections.
Extracting Lexically Divergent Paraphrases from Twitter
We present MultiP (Multi-instance Learning Paraphrase Model), a new model suited to identify paraphrases within the short messages on Twitter.
Inferring User Preferences by Probabilistic Logical Reasoning over Social Networks
by building a probabilistic model that reasons over user attributes (the user's location or gender) and the social network (the user's friends and spouse), via inferences like homophily (I am more likely to like sushi if spouse or friends like sushi, I am more likely to like the Knicks if I live in New York).
Sense Discovery via Co-Clustering on Images and Text
We present a co-clustering framework that can be used to discover multiple semantic and visual senses of a given Noun Phrase (NP).
Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks
Inferring latent attributes of people online is an important social computing task, but requires integrating the many heterogeneous sources of information available on the web.
Deep Reinforcement Learning for Dialogue Generation
Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes.
TweeTime: A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in Twitter
We describe TweeTIME, a temporal tagger for recognizing and normalizing time expressions in Twitter.
Results of the WNUT16 Named Entity Recognition Shared Task
This paper presents the results of the Twitter Named Entity Recognition shared task associated with W-NUT 2016: a named entity tagging task with 10 teams participating.
Adversarial Learning for Neural Dialogue Generation
In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.
Ranked #1 on
Dialogue Generation
on Amazon-5
"i have a feeling trump will win..................": Forecasting Winners and Losers from User Predictions on Twitter
Social media users often make explicit predictions about upcoming events.
``i have a feeling trump will win..................'': Forecasting Winners and Losers from User Predictions on Twitter
Social media users often make explicit predictions about upcoming events.
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols
We make our annotated Wet Lab Protocol Corpus available to the research community.
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
Neural conversation models tend to generate safe, generic responses for most inputs.
Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media
In this paper, we investigate methods to analyze the severity of cybersecurity threats based on the language that is used to describe them online.
Structured Minimally Supervised Learning for Neural Relation Extraction
Our approach achieves state-of-the-art results on minimally supervised sentential relation extraction, outperforming a number of baselines, including a competitive approach that uses the attention layer of a purely neural model.
SemEval-2016 Task 4: Sentiment Analysis in Twitter
The three new subtasks focus on two variants of the basic ``sentiment classification in Twitter'' task.
SemEval-2015 Task 10: Sentiment Analysis in Twitter
In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter.
SemEval-2014 Task 9: Sentiment Analysis in Twitter
We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014.
SemEval-2013 Task 2: Sentiment Analysis in Twitter
To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a message-level subtask.
An Empirical Study of Pre-trained Transformers for Arabic Information Extraction
Multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable the effective cross-lingual zero-shot transfer.
Code and Named Entity Recognition in StackOverflow
We also present the SoftNER model which achieves an overall 79. 10 F$_1$ score for code and named entity recognition on StackOverflow data.
Fluent Response Generation for Conversational Question Answering
In this work, we propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness.
Extracting a Knowledge Base of COVID-19 Events from Social Media
In this paper, we present a manually annotated corpus of 10, 000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied access to testing, claimed cures and preventions.
Measuring Forecasting Skill from Text
We present a number of linguistic metrics which are computed over text associated with people's predictions about the future including: uncertainty, readability, and emotion.
Model Selection for Cross-Lingual Transfer using a Learned Scoring Function
In the zero-shot cross-lingual transfer setting, only English training data is assumed, and the fine-tuned model is evaluated on another target language.
Model Selection for Cross-Lingual Transfer
Transformers that are pre-trained on multilingual corpora, such as, mBERT and XLM-RoBERTa, have achieved impressive cross-lingual transfer capabilities.
WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols
This paper presents the results of the wet lab information extraction task at WNUT 2020.
Ranked #1 on
Relation Extraction
on WNUT 2020
Process-Level Representation of Scientific Protocols with Interactive Annotation
We develop Process Execution Graphs (PEG), a document-level representation of real-world wet lab biochemistry protocols, addressing challenges such as cross-sentence relations, long-range coreference, grounding, and implicit arguments.
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts
To better understand the dynamics of contextually offensive language, we investigate the stance of dialogue model responses in offensive Reddit conversations.
Pre-train or Annotate? Domain Adaptation with a Constrained Budget
Our experiments suggest task-specific data annotation should be part of an economical strategy when adapting an NLP model to a new domain.
SynKB: Semantic Search for Synthetic Procedures
In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols.
Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts
As far as we are aware, this is the first work to present experimental results demonstrating the effectiveness of in-context learning on the task of few-shot anaphora resolution in scientific protocols.
Stanceosaurus: Classifying Stance Towards Multilingual Misinformation
We present Stanceosaurus, a new corpus of 28, 033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims.
Frustratingly Easy Label Projection for Cross-lingual Transfer
Translating training data into many languages has emerged as a practical solution for improving cross-lingual transfer.
Ranked #1 on
Cross-Lingual NER
on MasakhaNER2.0
Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments
We present a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them.
Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023?
In this paper, we evaluate the generalization of over 20 different models trained on CoNLL-2003, and show that NER models have very different generalization.
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
In this study, we introduce InfoSeek, a visual question answering dataset tailored for information-seeking questions that cannot be answered with only common sense knowledge.
Ranked #3 on
Visual Question Answering (VQA)
on InfoSeek
Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models
Fine-tuning large models is highly effective, however, inference can be expensive and produces carbon emissions.
Schema-Driven Information Extraction from Heterogeneous Tables
We use this collection of annotated tables to evaluate the ability of open-source and API-based language models to extract information from tables covering diverse domains and data formats.
Ranked #1 on
Attribute Extraction
on SWDE
Better Low-Resource Entity Recognition Through Translation and Annotation Fusion
Pre-trained multilingual language models have enabled significant advancements in cross-lingual transfer.
Cross-Lingual Transfer
Low Resource Named Entity Recognition
+4
Having Beer after Prayer? Measuring Cultural Bias in Large Language Models
In this paper, we show that multilingual and Arabic monolingual LMs exhibit bias towards entities associated with Western culture.
Are Large Language Models Robust Coreference Resolvers?
Recent work on extending coreference resolution across domains and languages relies on annotated data in both the target domain and language.
Instruction Tuning with Lexicons for Zero-Shot Style Classification
Style is used to convey authors' intentions and attitudes.
Improved Instruction Ordering in Recipe-Grounded Conversation
In this paper, we study the task of instructional dialogue and focus on the cooking domain.
Self-Specialization: Uncovering Latent Expertise within Large Language Models
Recent works have demonstrated the effectiveness of self-alignment in which a large language model is, by itself, aligned to follow general instructions through the automatic generation of instructional data using a handful of human-written seeds.
Can Language Models be Instructed to Protect Personal Information?
While data leaks should be prevented, it is also crucial to examine the trade-off between the privacy protection and model utility of proposed approaches.
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game
Our benchmark results show that many models are vulnerable to the attack strategies in the Tensor Trust dataset.
Reducing Privacy Risks in Online Self-Disclosures with Language Models
Motivated by the user feedback, we introduce the task of self-disclosure abstraction, which is paraphrasing disclosures into less specific terms while preserving their utility, e. g., "Im 16F" to "I'm a teenage girl".
UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text descriptions, searching for a news article with a headline image, or finding a similar photo with a query image.
Constrained Decoding for Cross-lingual Label Projection
Therefore, it is common to exploit translation and label projection to further improve the performance by (1) translating training data that is available in a high-resource language (e. g., English) together with the gold labels into low-resource languages, and/or (2) translating test data in low-resource languages to a high-source language to run inference on, then projecting the predicted span-level labels back onto the original test data.
Stanceosaurus 2.0: Classifying Stance Towards Russian and Spanish Misinformation
The Stanceosaurus corpus (Zheng et al., 2022) was designed to provide high-quality, annotated, 5-way stance data extracted from Twitter, suitable for analyzing cross-cultural and cross-lingual misinformation.
NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms
The performance of Large Language Models (LLMs) degrades from the temporal drift between data used for model training and newer text seen during inference.
