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Developing systems that understand, generate, and reason with human language across diverse contexts and modalities, enabling more natural and effective human-AI communication.

Overview

Natural Language Processing (NLP) sits at the intersection of linguistics, computer science, and artificial intelligence. Our research focuses on creating systems that can truly understand the nuance, context, and meaning embedded in human language.

We tackle fundamental challenges in language understanding including semantic interpretation, pragmatic reasoning, multilingual understanding, and generation of coherent, contextually appropriate text across various domains and applications.

Current Research Focus

Language Understanding and Reasoning

We develop models that go beyond surface-level pattern matching to grasp deeper meaning, context, and intent. This includes work on semantic parsing, coreference resolution, discourse understanding, and commonsense reasoning about language.

Large Language Models

Our research explores the capabilities and limitations of large-scale language models. We investigate pre-training strategies, fine-tuning techniques, prompting methods, and ways to make these powerful models more controllable, reliable, and aligned with human values.

Multilingual and Cross-Lingual NLP

Language technology should work for everyone, regardless of which language they speak. We develop models and techniques that can transfer knowledge across languages, handle code-switching, and provide high-quality NLP capabilities for low-resource languages.

Dialogue and Conversation

Building systems that can engage in natural, coherent, multi-turn conversations requires understanding context, maintaining consistency, and managing pragmatic aspects of communication. Our work addresses dialogue state tracking, response generation, and conversational reasoning.

Breakthrough Applications

• Machine Translation: Breaking down language barriers with high-quality translation systems that preserve meaning and style
• Question Answering: Building systems that can find and synthesize information from vast text corpora
• Text Summarization: Automatically condensing long documents while preserving key information and insights
• Sentiment Analysis: Understanding emotions, opinions, and attitudes expressed in text for business intelligence
• Content Generation: Creating coherent, contextually appropriate text for various applications from creative writing to technical documentation

Current Challenges

Despite remarkable progress, significant challenges remain including handling ambiguity and context-dependent meaning, ensuring factual accuracy and reducing hallucinations, detecting and mitigating biases in language models, understanding and generating figurative language, and creating more sample-efficient learning approaches.

Impact and Future Directions

Natural language processing has fundamentally changed how we interact with technology. From voice assistants to real-time translation, from content moderation to medical document analysis, NLP systems are becoming increasingly integral to modern life.

Looking ahead, we see opportunities in developing more robust and reliable language understanding, better handling of rare and low-resource languages, improved reasoning and world knowledge integration, more controllable and steerable generation systems, and enhanced privacy-preserving NLP techniques.

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