Search Results for author:
Found 31 papers, 18 papers with code
CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command Recognition
With the rise of deep learning and intelligent vehicles, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities.
Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension
Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task.
Thank You, Stingray: Multilingual Large Language Models Can Not (Yet) Disambiguate Cross-Lingual Word Sense
In this paper, we demonstrate using false friends -- words that are orthographically similar but have completely different meanings in two languages -- as a possible approach to pinpoint the limitation of cross-lingual sense disambiguation in LLMs.
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines
This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date.
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages
Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1, 300 indigenous languages and a population of 671 million people.
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.
Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages
To bridge this quality gap, we introduce Cendol, a collection of Indonesian LLMs encompassing both decoder-only and encoder-decoder architectures across a range of model sizes.
LLMs Are Few-Shot In-Context Low-Resource Language Learners
Our study concludes the significance of few-shot in-context information on enhancing the low-resource understanding quality of LLMs through semantically relevant information by closing the language gap in the target language and aligning the semantics between the targeted low-resource and the high-resource language that the model is proficient in.
Contrastive Learning for Inference in Dialogue
Inference, especially those derived from inductive processes, is a crucial component in our conversation to complement the information implicitly or explicitly conveyed by a speaker.
InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
We present InstructTODS, a novel off-the-shelf framework for zero-shot end-to-end task-oriented dialogue systems that can adapt to diverse domains without fine-tuning.
Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models
Object hallucination poses a significant challenge in vision-language (VL) models, often leading to the generation of nonsensical or unfaithful responses with non-existent objects.
Survey of Social Bias in Vision-Language Models
This survey aims to provide researchers with a high-level insight into the similarities and differences of social bias studies in pre-trained models across NLP, CV, and VL.
NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages
We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets.
PICK: Polished & Informed Candidate Scoring for Knowledge-Grounded Dialogue Systems
Grounding dialogue response generation on external knowledge is proposed to produce informative and engaging responses.
Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition
In this work, we analyze the transferability of emotion recognition across three different languages--English, Mandarin Chinese, and Cantonese; and 2 different age groups--adults and the elderly.
InstructAlign: High-and-Low Resource Language Alignment via Continual Crosslingual Instruction Tuning
Our results demonstrate the effectiveness of InstructAlign in enabling the model to understand low-resource languages with limited parallel data while preventing catastrophic forgetting.
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.
Which One Are You Referring To? Multimodal Object Identification in Situated Dialogue
The demand for multimodal dialogue systems has been rising in various domains, emphasizing the importance of interpreting multimodal inputs from conversational and situational contexts.
A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
It is, for example, better at deductive than inductive reasoning.
NusaCrowd: Open Source Initiative for Indonesian NLP Resources
We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language Modeling
Large pre-trained language models (LMs) have been widely adopted in biomedical and clinical domains, introducing many powerful LMs such as bio-lm and BioELECTRA.
What Did I Just Hear? Detecting Pornographic Sounds in Adult Videos Using Neural Networks
Audio-based pornographic detection enables efficient adult content filtering without sacrificing performance by exploiting distinct spectral characteristics.
Every picture tells a story: Image-grounded controllable stylistic story generation
Generating a short story out of an image is arduous.
NusaCrowd: A Call for Open and Reproducible NLP Research in Indonesian Languages
At the center of the underlying issues that halt Indonesian natural language processing (NLP) research advancement, we find data scarcity.
Speech Artifact Removal from EEG Recordings of Spoken Word Production with Tensor Decomposition
Research about brain activities involving spoken word production is considerably underdeveloped because of the undiscovered characteristics of speech artifacts, which contaminate electroencephalogram (EEG) signals and prevent the inspection of the underlying cognitive processes.
Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension
Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task.
CI-AVSR: A Cantonese Audio-Visual Speech Dataset for In-car Command Recognition
With the rise of deep learning and intelligent vehicle, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities.
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset
We further conduct experiments with Fairseq S2T Transformer, a state-of-the-art ASR model, on the biggest existing dataset, Common Voice zh-HK, and our proposed MDCC, and the results show the effectiveness of our dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation
ASCEND (A Spontaneous Chinese-English Dataset) is a high-quality Mandarin Chinese-English code-switching corpus built on spontaneous multi-turn conversational dialogue sources collected in Hong Kong.
Greenformer: Factorization Toolkit for Efficient Deep Neural Networks
While the recent advances in deep neural networks (DNN) bring remarkable success, the computational cost also increases considerably.
Nora: The Well-Being Coach
The current pandemic has forced people globally to remain in isolation and practice social distancing, which creates the need for a system to combat the resulting loneliness and negative emotions.
