Search Results for author:
Found 99 papers, 29 papers with code
Normalization and Back-Transliteration for Code-Switched Data
Code-switching is an omnipresent phenomenon in multilingual communities all around the world but remains a challenge for NLP systems due to the lack of proper data and processing techniques.
A Case Study of Deep Learning-Based Multi-Modal Methods for Labeling the Presence of Questionable Content in Movie Trailers
In this work, we explore different approaches to combine modalities for the problem of automated age-suitability rating of movie trailers.
Enhancing NER Performance in Low-Resource Pakistani Languages using Cross-Lingual Data Augmentation
Named Entity Recognition (NER), a fundamental task in Natural Language Processing (NLP), has shown significant advancements for high-resource languages.
Enhancing Depression Detection via Question-wise Modality Fusion
The performance of our framework is comparable to the current state-of-the-art models on the E-DAIC dataset and enhances interpretability by predicting scores for each question.
A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions
Language in the Arab world presents a complex diglossic and multilingual setting, involving the use of Modern Standard Arabic, various dialects and sub-dialects, as well as multiple European languages.
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
In pursuit of culturally diverse global multimodal models, our proposed All Languages Matter Benchmark (ALM-bench) represents the largest and most comprehensive effort to date for evaluating LMMs across 100 languages.
ProverbEval: Exploring LLM Evaluation Challenges for Low-resource Language Understanding
In this work, we explore LLM evaluation challenges for low-resource language understanding and introduce ProverbEval, LLM evaluation benchmark for low-resource languages based on proverbs to focus on low-resource language understanding in culture-specific scenarios.
The Zeno's Paradox of `Low-Resource' Languages
The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced.
RelUNet: Relative Channel Fusion U-Net for Multichannel Speech Enhancement
Neural multi-channel speech enhancement models, in particular those based on the U-Net architecture, demonstrate promising performance and generalization potential.
HyperLoader: Integrating Hypernetwork-Based LoRA and Adapter Layers into Multi-Task Transformers for Sequence Labelling
We present HyperLoader, a simple approach that combines different parameter-efficient fine-tuning methods in a multi-task setting.
The Privileged Students: On the Value of Initialization in Multilingual Knowledge Distillation
Knowledge distillation (KD) has proven to be a successful strategy to improve the performance of smaller models in many NLP tasks.
Labeling Comic Mischief Content in Online Videos with a Multimodal Hierarchical-Cross-Attention Model
To tackle this problem, we propose a novel end-to-end multimodal system for the task of comic mischief detection.
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data.
ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA
Aspect-Based Sentiment Analysis (ABSA) has experienced tremendous expansion and diversity due to various shared tasks spanning several languages and fields and organized via SemEval workshops and Germeval.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
What Can Natural Language Processing Do for Peer Review?
We hope that our work will help set the agenda for research in machine-assisted scientific quality control in the age of AI, within the NLP community and beyond.
Interpreting Themes from Educational Stories
Reading comprehension continues to be a crucial research focus in the NLP community.
NLP Progress in Indigenous Latin American Languages
The paper focuses on the marginalization of indigenous language communities in the face of rapid technological advancements.
Adaptive Cross-lingual Text Classification through In-Context One-Shot Demonstrations
Zero-Shot Cross-lingual Transfer (ZS-XLT) utilizes a model trained in a source language to make predictions in another language, often with a performance loss.
SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages
We present the first shared task on Semantic Textual Relatedness (STR).
Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering
We present Q-ViD, a simple approach for video question answering (video QA), that unlike prior methods, which are based on complex architectures, computationally expensive pipelines or use closed models like GPTs, Q-ViD relies on a single instruction-aware open vision-language model (InstructBLIP) to tackle videoQA using frame descriptions.
Ranked #14 on
Zero-Shot Video Question Answer
on NExT-QA
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages
Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks.
OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) delves into understanding sentiments specific to distinct elements within a user-generated review.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Positive and Risky Message Assessment for Music Products
In this work, we introduce a pioneering research challenge: evaluating positive and potentially harmful messages within music products.
Context-aware Adversarial Attack on Named Entity Recognition
In recent years, large pre-trained language models (PLMs) have achieved remarkable performance on many natural language processing benchmarks.
Overview of GUA-SPA at IberLEF 2023: Guarani-Spanish Code Switching Analysis
We present the first shared task for detecting and analyzing code-switching in Guarani and Spanish, GUA-SPA at IberLEF 2023.
SafeWebUH at SemEval-2023 Task 11: Learning Annotator Disagreement in Derogatory Text: Comparison of Direct Training vs Aggregation
Subjectivity and difference of opinion are key social phenomena, and it is crucial to take these into account in the annotation and detection process of derogatory textual content.
Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages
While code-mixing is a common linguistic practice in many parts of the world, collecting high-quality and low-cost code-mixed data remains a challenge for natural language processing (NLP) research.
Distillation of encoder-decoder transformers for sequence labelling
Driven by encouraging results on a wide range of tasks, the field of NLP is experiencing an accelerated race to develop bigger language models.
The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges
Code-Switching, a common phenomenon in written text and conversation, has been studied over decades by the natural language processing (NLP) research community.
Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition
In this work, we take the named entity recognition task in the English language as a case study and explore style transfer as a data augmentation method to increase the size and diversity of training data in low-resource scenarios.
Survey of Aspect-based Sentiment Analysis Datasets
Aspect-based sentiment analysis (ABSA) is a natural language processing problem that requires analyzing user-generated reviews to determine: a) The target entity being reviewed, b) The high-level aspect to which it belongs, and c) The sentiment expressed toward the targets and the aspects.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
CALCS 2021 Shared Task: Machine Translation for Code-Switched Data
For the unsupervised setting, we provide the following language pairs: English and Spanish-English (Eng-Spanglish), and English and Modern Standard Arabic-Egyptian Arabic (Eng-MSAEA) in both directions.
Exploring Conditional Text Generation for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is an NLP task that entails processing user-generated reviews to determine (i) the target being evaluated, (ii) the aspect category to which it belongs, and (iii) the sentiment expressed towards the target and aspect pair.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
From None to Severe: Predicting Severity in Movie Scripts
In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script.
Data Augmentation for Cross-Domain Named Entity Recognition
Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models.
Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crisp
Performance of neural models for named entity recognition degrades over time, becoming stale.
White Paper - Creating a Repository of Objectionable Online Content: Addressing Undesirable Biases and Ethical Considerations
This white paper summarizes the authors' structured brainstorming regarding ethical considerations for creating an extensive repository of online content labeled with tags that describe potentially questionable content for young viewers.
A Case Study of Deep Learning Based Multi-Modal Methods for Predicting the Age-Suitability Rating of Movie Trailers
In this work, we explore different approaches to combine modalities for the problem of automated age-suitability rating of movie trailers.
White Paper: Challenges and Considerations for the Creation of a Large Labelled Repository of Online Videos with Questionable Content
This white paper presents a summary of the discussions regarding critical considerations to develop an extensive repository of online videos annotated with labels indicating questionable content.
Learning to Emphasize: Dataset and Shared Task Models for Selecting Emphasis in Presentation Slides
We evaluate a range of state-of-the-art models on this novel dataset by organizing a shared task and inviting multiple researchers to model emphasis in this new domain.
Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality
To alleviate these challenges, we propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT.
Can images help recognize entities? A study of the role of images for Multimodal NER
Multimodal named entity recognition (MNER) requires to bridge the gap between language understanding and visual context.
SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets
In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020).
SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media
In this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media.
LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation
To facilitate research in this direction, we propose a centralized benchmark for Linguistic Code-switching Evaluation (LinCE) that combines ten corpora covering four different code-switched language pairs (i. e., Spanish-English, Nepali-English, Hindi-English, and Modern Standard Arabic-Egyptian Arabic) and four tasks (i. e., language identification, named entity recognition, part-of-speech tagging, and sentiment analysis).
Let Me Choose: From Verbal Context to Font Selection
In this paper, we aim to learn associations between visual attributes of fonts and the verbal context of the texts they are typically applied to.
Aggression and Misogyny Detection using BERT: A Multi-Task Approach
In recent times, the focus of the NLP community has increased towards offensive language, aggression, and hate-speech detection. This paper presents our system for TRAC-2 shared task on {``}Aggression Identification{''} (sub-task A) and {``}Misogynistic Aggression Identification{''} (sub-task B).
Detecting Early Signs of Cyberbullying in Social Media
We also investigate the possibility of designing a framework to monitor the streams of users{'} online messages and detects the signs of cyberbullying as early as possible.
Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues
Movies help us learn and inspire societal change.
Overview for the Second Shared Task on Language Identification in Code-Switched Data
We present an overview of the second shared task on language identification in code-switched data.
Part of speech tagging for code switched data
We address the problem of Part of Speech tagging (POS) in the context of linguistic code switching (CS).
From English to Code-Switching: Transfer Learning with Strong Morphological Clues
We show the effectiveness of this transfer learning step by outperforming multilingual BERT and homologous CS-unaware ELMo models and establishing a new state of the art in CS tasks, such as NER and POS tagging.
Dependency-Aware Named Entity Recognition with Relative and Global Attentions
On the other hand, the global attention spots the most relevant words in the sequence.
Attending the Emotions to Detect Online Abusive Language
We experiment with this model on our dataset and later present the analysis.
Jointly Learning Author and Annotated Character N-gram Embeddings: A Case Study in Literary Text
An author{'}s way of presenting a story through his/her writing style has a great impact on whether the story will be liked by readers or not.
Multi-view Story Characterization from Movie Plot Synopses and Reviews
This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies.
Rating for Parents: Predicting Children Suitability Rating for Movies Based on Language of the Movies
In this paper, our goal is to predict the suitability of the movie content for children and young adults based on scripts.
Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions
In visual communication, text emphasis is used to increase the comprehension of written text to convey the author{'}s intent.
A Multi-task Approach for Named Entity Recognition in Social Media Data
Named Entity Recognition for social media data is challenging because of its inherent noisiness.
Ranked #22 on
Named Entity Recognition (NER)
on WNUT 2017
Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data.
Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media
Our systems outperform the current F1 scores of the state of the art on the Workshop on Noisy User-generated Text 2017 dataset by 2. 45% and 3. 69%, establishing a more suitable approach for social media environments.
Ranked #18 on
Named Entity Recognition (NER)
on WNUT 2017
Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Models
The proposed dataset Stack Overflow is a useful resource to develop novel solutions, specifically data-hungry neural network models, for the prediction of relatedness in technical community question-answering forums.
A Genre-Aware Attention Model to Improve the Likability Prediction of Books
Likability prediction of books has many uses.
Folksonomication: Predicting Tags for Movies from Plot Synopses Using Emotion Flow Encoded Neural Network
Folksonomy of movies covers a wide range of heterogeneous information about movies, like the genre, plot structure, visual experiences, soundtracks, metadata, and emotional experiences from watching a movie.
RiTUAL-UH at TRAC 2018 Shared Task: Aggression Identification
This paper presents our system for "TRAC 2018 Shared Task on Aggression Identification".
UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering
In this work we describe the system built for the three English subtasks of the SemEval 2016 Task 3 by the Department of Computer Science of the University of Houston (UH) and the Pattern Recognition and Human Language Technology (PRHLT) research center - Universitat Polit`ecnica de Val`encia: UH-PRHLT.
Language Identification and Analysis of Code-Switched Social Media Text
In this paper, we detail our work on comparing different word-level language identification systems for code-switched Hindi-English data and a standard Spanish-English dataset.
Early Text Classification Using Multi-Resolution Concept Representations
The intensive use of e-communications in everyday life has given rise to new threats and risks.
Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books
Books have the power to make us feel happiness, sadness, pain, surprise, or sorrow.
MPST: A Corpus of Movie Plot Synopses with Tags
In this paper, we set out to the task of collecting a corpus of movie plot synopses and tags.
RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks
In this paper, we present our systems for the {``}SemEval-2017 Task-5 on Fine-Grained Sentiment Analysis on Financial Microblogs and News{''}.
Detecting Nastiness in Social Media
Although social media has made it easy for people to connect on a virtually unlimited basis, it has also opened doors to people who misuse it to undermine, harass, humiliate, threaten and bully others.
Convolutional Neural Networks for Authorship Attribution of Short Texts
We present a model to perform authorship attribution of tweets using Convolutional Neural Networks (CNNs) over character n-grams.
A Multi-task Approach to Predict Likability of Books
We investigate the value of feature engineering and neural network models for predicting successful writing.
Gated Multimodal Units for Information Fusion
The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities.
CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings
The evaluation results of our system on the test set is 88. 1{\%} (79. 0{\%} for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76. 0{\%} (42. 3{\%} when excluding RANDOM) for Task-2 on identifying finer-grained semantic relations.
Age and Gender Prediction on Health Forum Data
Health support forums have become a rich source of data that can be used to improve health care outcomes.
Evaluation of YTEX and MetaMap for clinical concept recognition
We used MetaMap and YTEX as a basis for the construc- tion of two separate systems to participate in the 2013 ShARe/CLEF eHealth Task 1[9], the recognition of clinical concepts.
Sockpuppet Detection in Wikipedia: A Corpus of Real-World Deceptive Writing for Linking Identities
This paper describes the corpus of sockpuppet cases we gathered from Wikipedia.
