activity
20122026
most citedDeep Learning applied to NLP

129 citations · 717 across the 46 of their papers we have counts for

collaborators
Showing 2022Show all

13 papers · 1 filter

cs.LG2022

Utilizing Priming to Identify Optimal Class Ordering to Alleviate Catastrophic Forgetting

Gabriel Mantione-Holmes, Justin Leo, Jugal Kalita

In order for artificial neural networks to begin accurately mimicking biological ones, they must be able to adapt to new exigencies without forgetting what they have learned from p…

cs.CL2022

CAMeMBERT: Cascading Assistant-Mediated Multilingual BERT

Dan DeGenaro, Jugal Kalita

Large language models having hundreds of millions, and even billions, of parameters have performed extremely well on a variety of natural language processing (NLP) tasks. Their wid…

cs.CV2022★ 14 cited

Using Artificial Intelligence and IoT for Constructing a Smart Trash Bin

Khang Nhut Lam, Nguyen Hoang Huynh, Nguyen Bao Ngoc +6

The research reported in this paper transforms a normal trash bin into a smarter one by applying computer vision technology. With the support of sensors and actuator devices, the t…

cs.CV2022★ 5 cited

Facial Expression Recognition and Image Description Generation in Vietnamese

Khang Nhut Lam, Kim-Ngoc Thi Nguyen, Loc Huu Nguy +1

This paper discusses a facial expression recognition model and a description generation model to build descriptive sentences for images and facial expressions of people in images.…

cs.CL2022

Automatically Creating a Large Number of New Bilingual Dictionaries

Khang Nhut Lam, Feras Al Tarouti, Jugal Kalita

This paper proposes approaches to automatically create a large number of new bilingual dictionaries for low-resource languages, especially resource-poor and endangered languages, f…

cs.IR2022★ 19 cited

Building a Chatbot on a Closed Domain using RASA

Khang Nhut Lam, Nam Nhat Le, Jugal Kalita

In this study, we build a chatbot system in a closed domain with the RASA framework, using several models such as SVM for classifying intents, CRF for extracting entities and LSTM…

cs.CL2022★ 8 cited

Creating Lexical Resources for Endangered Languages

Khang Nhut Lam, Feras Al Tarouti, Jugal Kalita

This paper examines approaches to generate lexical resources for endangered languages. Our algorithms construct bilingual dictionaries and multilingual thesauruses using public Wor…

cs.CL2022★ 23 cited

Automatically constructing Wordnet synsets

Khang Nhut Lam, Feras Al Tarouti, Jugal Kalita

Manually constructing a Wordnet is a difficult task, needing years of experts' time. As a first step to automatically construct full Wordnets, we propose approaches to generate Wor…

cs.CL2022★ 12 cited

Creating Reverse Bilingual Dictionaries

Khang Nhut Lam, Jugal Kalita

Bilingual dictionaries are expensive resources and not many are available when one of the languages is resource-poor. In this paper, we propose algorithms for creation of new rever…

cs.CL2022★ 1 cited

Phrase translation using a bilingual dictionary and n-gram data: A case study from Vietnamese to English

Khang Nhut Lam, Feras Al Tarouti, Jugal Kalita

Past approaches to translate a phrase in a language L1 to a language L2 using a dictionary-based approach require grammar rules to restructure initial translations. This paper intr…

cs.CV2022

Towards Multimodal Vision-Language Models Generating Non-Generic Text

Wes Robbins, Zanyar Zohourianshahzadi, Jugal Kalita

Vision-language models can assess visual context in an image and generate descriptive text. While the generated text may be accurate and syntactically correct, it is often overly g…

cs.CV2022★ 1 cited

ZoDIAC: Zoneout Dropout Injection Attention Calculation

Zanyar Zohourianshahzadi, Terrance E. Boult, Jugal K. Kalita

In the past few years the transformer model has been utilized for a variety of tasks such as image captioning, image classification natural language generation, and natural languag…

cs.CL2022★ 3 cited

Using Random Perturbations to Mitigate Adversarial Attacks on Sentiment Analysis Models

Abigail Swenor, Jugal Kalita

Attacks on deep learning models are often difficult to identify and therefore are difficult to protect against. This problem is exacerbated by the use of public datasets that typic…