Search Results for author:
Found 33 papers, 13 papers with code
Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts
Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER).
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification
The experimental landscape in natural language processing for social media is too fragmented.
Ranked #3 on
Sentiment Analysis
on TweetEval
TimeLMs: Diachronic Language Models from Twitter
Despite its importance, the time variable has been largely neglected in the NLP and language model literature.
TweetNLP: Cutting-Edge Natural Language Processing for Social Media
In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media.
Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning
In addition, our model can extract visual information as suggested by the text prompt, e. g., "an object in image one is moving northeast", and generate corresponding videos.
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond
Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention.
Ranked #2 on
Sentiment Analysis
on TweetEval
Learning Cross-lingual Embeddings from Twitter via Distant Supervision
Cross-lingual embeddings represent the meaning of words from different languages in the same vector space.
The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks
Contextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity in recent years.
Multimodal Emoji Prediction
Emojis are small images that are commonly included in social media text messages.
Are Emojis Predictable?
Emojis are ideograms which are naturally combined with plain text to visually complement or condense the meaning of a message.
How Gender and Skin Tone Modifiers Affect Emoji Semantics in Twitter
Our analyses reveal that some stereotypes related to the skin color and gender seem to be reflected on the use of these modifiers.
TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media
To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift.
PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning
Instruction tuning has remarkably advanced large language models (LLMs) in understanding and responding to diverse human instructions.
Exploring Emoji Usage and Prediction Through a Temporal Variation Lens
The frequent use of Emojis on social media platforms has created a new form of multimodal social interaction.
Interpretable Emoji Prediction via Label-Wise Attention LSTMs
Human language has evolved towards newer forms of communication such as social media, where emojis (i. e., ideograms bearing a visual meaning) play a key role.
SemEval 2018 Task 2: Multilingual Emoji Prediction
This paper describes the results of the first Shared Task on Multilingual Emoji Prediction, organized as part of SemEval 2018.
Towards the Understanding of Gaming Audiences by Modeling Twitch Emotes
Videogame streaming platforms have become a paramount example of noisy user-generated text.
Modelling Irony in Twitter: Feature Analysis and Evaluation
We propose in this paper a new set of experiments to assess the relevance of the features included in our model.
What does this Emoji Mean? A Vector Space Skip-Gram Model for Twitter Emojis
Emojis allow us to describe objects, situations and even feelings with small images, providing a visual and quick way to communicate.
Efficient Learning of Less Biased Models with Transfer Learning
Prediction bias in machine learning models, referring to undesirable model behaviors that discriminates inputs mentioning or produced by certain group, has drawn increasing attention from the research community given its societal impact.
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning
Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution.
Twitter Topic Classification
Social media platforms host discussions about a wide variety of topics that arise everyday.
SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis
We propose MINT, a new Multilingual INTimacy analysis dataset covering 13, 372 tweets in 10 languages including English, French, Spanish, Italian, Portuguese, Korean, Dutch, Chinese, Hindi, and Arabic.
Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twitter
This paper introduces a large collection of time series data derived from Twitter, postprocessed using word embedding techniques, as well as specialized fine-tuned language models.
Context-Aware Prediction of User Engagement on Online Social Platforms
The success of online social platforms hinges on their ability to predict and understand user behavior at scale.
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research
Despite its relevance, the maturity of NLP for social media pales in comparison with general-purpose models, metrics and benchmarks.
Designing and Evaluating General-Purpose User Representations Based on Behavioral Logs from a Measurement Process Perspective: A Case Study with Snapchat
Using this new framework, we design a Transformer-based user model that can produce high-quality general-purpose user representations for instant messaging platforms like Snapchat.
Evaluating Very Long-Term Conversational Memory of LLM Agents
Using this pipeline, we collect LoCoMo, a dataset of very long-term conversations, each encompassing 300 turns and 9K tokens on avg., over up to 35 sessions.
USE: Dynamic User Modeling with Stateful Sequence Models
Furthermore, we introduce a novel training objective named future W-behavior prediction to transcend the limitations of next-token prediction by forecasting a broader horizon of upcoming user behaviors.
