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Recent advances in deep learning based dialogue systems: A systematic survey
Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …
real-life applications. It is also a complicated task since many NLP tasks deserving study are …
A causal lens for controllable text generation
Controllable text generation concerns two fundamental tasks of wide applications, namely
generating text of given attributes (ie, attribute-conditional generation), and minimally editing …
generating text of given attributes (ie, attribute-conditional generation), and minimally editing …
Cpl: Counterfactual prompt learning for vision and language models
Prompt tuning is a new few-shot transfer learning technique that only tunes the learnable
prompt for pre-trained vision and language models such as CLIP. However, existing prompt …
prompt for pre-trained vision and language models such as CLIP. However, existing prompt …
Learning to imagine: Integrating counterfactual thinking in neural discrete reasoning
Neural discrete reasoning (NDR) has shown remarkable progress in combining deep
models with discrete reasoning. However, we find that existing NDR solution suffers from …
models with discrete reasoning. However, we find that existing NDR solution suffers from …
Ifqa: A dataset for open-domain question answering under counterfactual presuppositions
Although counterfactual reasoning is a fundamental aspect of intelligence, the lack of large-
scale counterfactual open-domain question-answering (QA) benchmarks makes it difficult to …
scale counterfactual open-domain question-answering (QA) benchmarks makes it difficult to …
Empowering language understanding with counterfactual reasoning
Present language understanding methods have demonstrated extraordinary ability of
recognizing patterns in texts via machine learning. However, existing methods …
recognizing patterns in texts via machine learning. However, existing methods …
Improving domain generalization for prompt-aware essay scoring via disentangled representation learning
Abstract Automated Essay Scoring (AES) aims to score essays written in response to
specific prompts. Many AES models have been proposed, but most of them are either …
specific prompts. Many AES models have been proposed, but most of them are either …
[HTML][HTML] A recent survey on controllable text generation: A causal perspective
J Wang, C Zhang, D Zhang, H Tong, C Yan… - Fundamental …, 2024 - Elsevier
As an important subject of natural language generation, Controllable Text Generation (CTG)
focuses on integrating additional constraints and controls while generating texts and has …
focuses on integrating additional constraints and controls while generating texts and has …
Distilling causal effect from miscellaneous other-class for continual named entity recognition
Continual Learning for Named Entity Recognition (CL-NER) aims to learn a growing number
of entity types over time from a stream of data. However, simply learning Other-Class in the …
of entity types over time from a stream of data. However, simply learning Other-Class in the …
Do large language models understand logic or just mimick context?
Over the past few years, the abilities of large language models (LLMs) have received
extensive attention, which have performed exceptionally well in complicated scenarios such …
extensive attention, which have performed exceptionally well in complicated scenarios such …