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Found 76 papers, 20 papers with code
SURREY-CTS-NLP at WASSA2022: An Experiment of Discourse and Sentiment Analysis for the Prediction of Empathy, Distress and Emotion
This paper summarises the submissions our team, SURREY-CTS-NLP has made for the WASSA 2022 Shared Task for the prediction of empathy, distress and emotion.
“So You Think You’re Funny?”: Rating the Humour Quotient in Standup Comedy
The normalized duration (laughter duration divided by the clip duration) of laughter in each clip is used to compute this humour coefficient score on a five-point scale (0-4).
Sophisticated Lexical Databases - Simplified Usage: Mobile Applications and Browser Plugins For Wordnets
India is a country with 22 officially recognized languages and 17 of these have WordNets, a crucial resource.
A picture is worth a thousand words: Using OpenClipArt library for enriching IndoWordNet
Our contribution is three fold: (1) We develop a system, which, given a synset in English, finds an appropriate image for the synset.
Mapping it differently: A solution to the linking challenges
This paper reports the work of creating bilingual mappings in English for certain synsets of Hindi wordnet, the need for doing this, the methods adopted and the tools created for the task.
Hindi Wordnet for Language Teaching: Experiences and Lessons Learnt
This aid is based on modern pedagogical axioms and is aligned to the learning objectives of the syllabi of the school education in India.
Synthesizing Audio for Hindi WordNet
We also develop voices using the existing implementations of the aforementioned systems, and (2) We use these voices to generate sample audios for randomly chosen words; manually evaluate the audio generated, and produce audio for all WordNet words using the winner voice model.
pyiwn: A Python based API to access Indian Language WordNets
Indian language WordNets have their individual web-based browsing interfaces along with a common interface for IndoWordNet.
Automatically Generating Chinese Homophone Words to Probe Machine Translation Estimation Systems
The generated Chinese homophones, along with their manual translations, are utilized to generate perturbations and to probe the robustness of existing quality evaluation models, including models trained using multi-task learning, fine-tuned variants of multilingual language models, as well as large language models (LLMs).
GCDance: Genre-Controlled 3D Full Body Dance Generation Driven By Music
To overcome these challenges, we propose GCDance, a classifier-free diffusion framework for generating genre-specific dance motions conditioned on both music and textual prompts.
Towards a Robust Framework for Multimodal Hate Detection: A Study on Video vs. Image-based Content
This paper presents a systematic analysis of fusion-based approaches for multimodal hate detection, focusing on their performance across video and image-based content.
Ranked #1 on
Hate Speech Detection
on HateMM
Giving the Old a Fresh Spin: Quality Estimation-Assisted Constrained Decoding for Automatic Post-Editing
Automatic Post-Editing (APE) systems often struggle with over-correction, where unnecessary modifications are made to a translation, diverging from the principle of minimal editing.
When LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages
This paper investigates the reference-less evaluation of machine translation for low-resource language pairs, known as quality estimation (QE).
PortraitTalk: Towards Customizable One-Shot Audio-to-Talking Face Generation
Audio-driven talking face generation is a challenging task in digital communication.
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English
Subsequently, we fine-tune nine large language models (LLMs) (representing a range of encoder/decoder and mono/multilingual models) on these datasets, and evaluate their performance on the two tasks.
A Survey of Multimodal Sarcasm Detection
In this paper, we present the first comprehensive survey on multimodal sarcasm detection - henceforth MSD - to date.
Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages
This exploratory study investigates the potential of multilingual Automatic Post-Editing (APE) systems to enhance the quality of machine translations for low-resource Indo-Aryan languages.
Centrality-aware Product Retrieval and Ranking
We introduce a User-intent Centrality Optimization (UCO) approach for existing models, which optimises for the user intent in semantic product search.
Experiences from Creating a Benchmark for Sentiment Classification for Varieties of English
Existing benchmarks often fail to account for linguistic diversity, like language variants of English.
Are Large Language Models State-of-the-art Quality Estimators for Machine Translation of User-generated Content?
This paper investigates whether large language models (LLMs) are state-of-the-art quality estimators for machine translation of user-generated content (UGC) that contains emotional expressions, without the use of reference translations.
Edit Distances and Their Applications to Downstream Tasks in Research and Commercial Contexts
The tutorial describes the concept of edit distances applied to research and commercial contexts.
What do Large Language Models Need for Machine Translation Evaluation?
For the evaluation of machine translation (MT), existing research shows that LLMs are able to achieve results comparable to fine-tuned multilingual pre-trained language models.
A Multi-task Learning Framework for Evaluating Machine Translation of Emotion-loaded User-generated Content
We extend it with sentence-level evaluation scores and word-level labels, leading to a dataset suitable for sentence- and word-level translation evaluation and emotion classification, in a multi-task setting.
Connecting Ideas in 'Lower-Resource' Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios
Despite excellent results on benchmarks over a small subset of languages, large language models struggle to process text from languages situated in `lower-resource' scenarios such as dialects/sociolects (national or social varieties of a language), Creoles (languages arising from linguistic contact between multiple languages) and other low-resource languages.
AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis
To obtain a material-aware and geometry-aware condition for audio synthesis, we learn an explicit point-based scene representation with an audio-guidance parameter on locally initialized Gaussian points, taking into account the space relation from the listener and sound source.
Unsupervised Audio-Visual Segmentation with Modality Alignment
Audio-Visual Segmentation (AVS) aims to identify, at the pixel level, the object in a visual scene that produces a given sound.
Google Translate Error Analysis for Mental Healthcare Information: Evaluating Accuracy, Comprehensibility, and Implications for Multilingual Healthcare Communication
This study explores the use of Google Translate (GT) for translating mental healthcare (MHealth) information and evaluates its accuracy, comprehensibility, and implications for multilingual healthcare communication through analysing GT output in the MHealth domain from English to Persian, Arabic, Turkish, Romanian, and Spanish.
Airavata: Introducing Hindi Instruction-tuned LLM
We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi.
Natural Language Processing for Dialects of a Language: A Survey
Motivated by the performance degradation of NLP models for dialectal datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creation
We propose a repair-filter-use methodology that uses an APE system to correct errors on the target side of the MT training data.
SurreyAI 2023 Submission for the Quality Estimation Shared Task
Quality Estimation (QE) systems are important in situations where it is necessary to assess the quality of translations, but there is no reference available.
CreoleVal: Multilingual Multitask Benchmarks for Creoles
Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research. While the genealogical ties between Creoles and a number of highly-resourced languages imply a significant potential for transfer learning, this potential is hampered due to this lack of annotated data.
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection
The introduction of the MUStARD dataset, and its emotion recognition extension MUStARD++, have identified sarcasm to be a multi-modal phenomenon -- expressed not only in natural language text, but also through manners of speech (like tonality and intonation) and visual cues (facial expression).
Leveraging Foundation models for Unsupervised Audio-Visual Segmentation
Particularly, in situations where existing supervised AVS methods struggle with overlapping foreground objects, our models still excel in accurately segmenting overlapped auditory objects.
Evaluation of Chinese-English Machine Translation of Emotion-Loaded Microblog Texts: A Human Annotated Dataset for the Quality Assessment of Emotion Translation
In this paper, we focus on how current Machine Translation (MT) tools perform on the translation of emotion-loaded texts by evaluating outputs from Google Translate according to a framework proposed in this paper.
Applications and Challenges of Sentiment Analysis in Real-life Scenarios
Sentiment analysis has benefited from the availability of lexicons and benchmark datasets created over decades of research.
Harnessing Abstractive Summarization for Fact-Checked Claim Detection
We propose a new workflow for efficiently detecting previously fact-checked claims that uses abstractive summarization to generate crisp queries.
HiNER: A Large Hindi Named Entity Recognition Dataset
We use different language models to perform the sequence labelling task for NER and show the efficacy of our data by performing a comparative evaluation with models trained on another dataset available for the Hindi NER task.
Ranked #1 on
Named Entity Recognition (NER)
on HiNER-original
PLOD: An Abbreviation Detection Dataset for Scientific Documents
This paper presents PLOD, a large-scale dataset for abbreviation detection and extraction that contains 160k+ segments automatically annotated with abbreviations and their long forms.
Ranked #1 on
AbbreviationDetection
on PLOD-unfiltered
An Ensemble Approach to Acronym Extraction using Transformers
Acronyms are abbreviated units of a phrase constructed by using initial components of the phrase in a text.
Indian Language Wordnets and their Linkages with Princeton WordNet
Linked wordnets are extensions of wordnets, which link similar concepts in wordnets of different languages.
Strategies of Effective Digitization of Commentaries and Sub-commentaries: Towards the Construction of Textual History
We use the text of the K\=a\'sik\=avrtti (KV) as a sample text, and with the help of philologists, we digitize the commentaries available to us.
Semi-automatic WordNet Linking using Word Embeddings
Thus techniques that can aid the experts are desirable.
Some Strategies to Capture Karaka-Yogyata with Special Reference to apadana
Our paper attempts to show the implication of the creation of our tool in this area.
Utilizing Wordnets for Cognate Detection among Indian Languages
Automatic Cognate Detection (ACD) is a challenging task which has been utilized to help NLP applications like Machine Translation, Information Retrieval and Computational Phylogenetics.
"A Passage to India": Pre-trained Word Embeddings for Indian Languages
In this paper, we use various existing approaches to create multiple word embeddings for 14 Indian languages.
A Survey on Using Gaze Behaviour for Natural Language Processing
Gaze behaviour has been used as a way to gather cognitive information for a number of years.
Challenge Dataset of Cognates and False Friend Pairs from Indian Languages
In this paper, we describe the creation of two cognate datasets for twelve Indian languages, namely Sanskrit, Hindi, Assamese, Oriya, Kannada, Gujarati, Tamil, Telugu, Punjabi, Bengali, Marathi, and Malayalam.
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages
We, then, evaluate the impact of our cognate detection mechanism on neural machine translation (NMT), as a downstream task.
Cross-Lingual Information Retrieval
Cross-Lingual Word Embeddings
+5
Cognition-aware Cognate Detection
We collect gaze behaviour data for a small sample of cognates and show that extracted cognitive features help the task of cognate detection.
Automated Evidence Collection for Fake News Detection
The results show that our approach outperforms the state-of-the-art methods in fake news detection to achieve an F1-score of 99. 25 over the dataset provided for the CONSTRAINT-2021 Shared Task.
"So You Think You're Funny?": Rating the Humour Quotient in Standup Comedy
We devise a novel scoring mechanism to annotate the training data with a humour quotient score using the audience's laughter.
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation
However, this ability is yet to be tested in the current evaluation practices, where QE systems are assessed only in terms of their correlation with human judgements.
Cognitively Aided Zero-Shot Automatic Essay Grading
Automatic essay grading (AEG) is a process in which machines assign a grade to an essay written in response to a topic, called the prompt.
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour
To demonstrate the efficacy of this multi-task learning based approach to automatic essay grading, we collect gaze behaviour for 48 essays across 4 essay sets, and learn gaze behaviour for the rest of the essays, numbering over 7000 essays.
``A Passage to India'': Pre-trained Word Embeddings for Indian Languages
In this paper, we use various existing approaches to create multiple word embeddings for 14 Indian languages.
Recommendation Chart of Domains for Cross-Domain Sentiment Analysis: Findings of A 20 Domain Study
Cross-domain sentiment analysis (CDSA) helps to address the problem of data scarcity in scenarios where labelled data for a domain (known as the target domain) is unavailable or insufficient.
Recommendation Chart of Domains for Cross-Domain Sentiment Analysis:Findings of A 20 Domain Study
Cross-domain sentiment analysis (CDSA) helps to address the problem of data scarcity in scenarios where labelled data for a domain (known as the target domain) is unavailable or insufficient.
Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour
Such subjective aspects are better handled using cognitive information.
New Vistas to study Bhartrhari: Cognitive NLP
The notions of a sentence and a word as a meaningful linguistic unit in the language have been a subject matter for the discussion in many works that followed later on.
Is your Statement Purposeless? Predicting Computer Science Graduation Admission Acceptance based on Statement Of Purpose
We present a quantitative, data-driven machine learning approach to mitigate the problem of unpredictability of Computer Science Graduate School Admissions.
Leveraging Cognitive Features for Sentiment Analysis
Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels.
Harnessing Cognitive Features for Sarcasm Detection
In this paper, we propose a novel mechanism for enriching the feature vector, for the task of sarcasm detection, with cognitive features extracted from eye-movement patterns of human readers.
Civique: Using Social Media to Detect Urban Emergencies
We present the Civique system for emergency detection in urban areas by monitoring micro blogs like Tweets.
SlangNet: A WordNet like resource for English Slang
We present a WordNet like structured resource for slang words and neologisms on the internet.
That'll Do Fine!: A Coarse Lexical Resource for English-Hindi MT, Using Polylingual Topic Models
As demonstrated by the quality of our coarse lexical resource and its benefit to MT, we believe that our sentential approach to create such a resource will help MT for resource-constrained languages.
