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- 2Natural Language Processing
- 1EM
- 1Inverse reinforcement learning
- 1Language Models
- 1Language models
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Fall 2022
Building intelligent open-domain dialogue systems is a long-standing goal of artificial intelligence. These systems, also known as chatbots, aim to hold conversations with humans in an open-ended fashion. However, it is well known that standard encoder-decoder dialogue systems tend to generate...
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Spring 2021
Paraphrasing involves changing the expression of a sentence and rewording it to inform the same information as the original sentence and can occur at word-level, phrase-level, or sentence-level. Paraphrasing task has been attracting attention in recent years as several natural language processing...
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Fall 2023
Language models are a fundamental component of natural language processing (NLP) systems. Numerous successful deployments of modern artificial intelligence systems are based on language models, including GPT-4 and ChatGPT. In practice, they are often trained with the teacher forcing objective,...
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Spring 2024
In the era of artificial intelligence, neural models have emerged as a powerful tool for tackling a wide range of tasks. However, these models are commonly regarded as black-box systems, making it difficult to understand their internal workings. The natural language explanation task seeks to...
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Spring 2024
Text attribute transfer (TAT) is a natural language processing task that involves transforming some attributes of a given text while preserving other attributes. Recently, prompting approaches have been explored in TAT with the emergence of various pretrained language models (PLMs), where a...
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Fall 2022
Sentence reconstruction and generation are essential applications in Natural Language Processing (NLP). Early studies were based on classic methods such as production rules and statistical models. Recently, the prevailing models typically use deep neural networks. In this study, we utilize deep...