Deep learning in sentiment analysis: Recent architectures
T Abdullah, A Ahmet - ACM Computing Surveys, 2022 - dl.acm.org
Humans are increasingly integrated with devices that enable the collection of vast
unstructured opinionated data. Accurately analysing subjective information from this data is …
unstructured opinionated data. Accurately analysing subjective information from this data is …
Balancing discriminability and transferability for source-free domain adaptation
Conventional domain adaptation (DA) techniques aim to improve domain transferability by
learning domain-invariant representations; while concurrently preserving the task …
learning domain-invariant representations; while concurrently preserving the task …
GPL: Generative pseudo labeling for unsupervised domain adaptation of dense retrieval
Dense retrieval approaches can overcome the lexical gap and lead to significantly improved
search results. However, they require large amounts of training data which is not available …
search results. However, they require large amounts of training data which is not available …
Coco-dr: Combating distribution shifts in zero-shot dense retrieval with contrastive and distributionally robust learning
We present a new zero-shot dense retrieval (ZeroDR) method, COCO-DR, to improve the
generalization ability of dense retrieval by combating the distribution shifts between source …
generalization ability of dense retrieval by combating the distribution shifts between source …
Low-resource dense retrieval for open-domain question answering: A comprehensive survey
Dense retrieval (DR) approaches based on powerful pre-trained language models (PLMs)
achieved significant advances and have become a key component for modern open-domain …
achieved significant advances and have become a key component for modern open-domain …
Domain adaptation for deep entity resolution
Entity resolution (ER) is a core problem of data integration. The state-of-the-art (SOTA)
results on ER are achieved by deep learning (DL) based methods, trained with a lot of …
results on ER are achieved by deep learning (DL) based methods, trained with a lot of …
On the domain adaptation and generalization of pretrained language models: A survey
Recent advances in NLP are brought by a range of large-scale pretrained language models
(PLMs). These PLMs have brought significant performance gains for a range of NLP tasks …
(PLMs). These PLMs have brought significant performance gains for a range of NLP tasks …
Vibe: Topic-driven temporal adaptation for twitter classification
Language features are evolving in real-world social media, resulting in the deteriorating
performance of text classification in dynamics. To address this challenge, we study temporal …
performance of text classification in dynamics. To address this challenge, we study temporal …
Domain confused contrastive learning for unsupervised domain adaptation
In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging self-
supervised approach. One of the difficulties is how to learn task discrimination in the …
supervised approach. One of the difficulties is how to learn task discrimination in the …
Climategpt: Towards ai synthesizing interdisciplinary research on climate change
This paper introduces ClimateGPT, a model family of domain-specific large language
models that synthesize interdisciplinary research on climate change. We trained two 7B …
models that synthesize interdisciplinary research on climate change. We trained two 7B …