Papers by Christopher Lin
Joint Multimedia Event Extraction from Video and Article (2021.findings-emnlp)
Copied to clipboard
Brian Chen, Xudong Lin, Christopher Thomas, Manling Li, Shoya Yoshida, Lovish Chum, Heng Ji, Shih-Fu Chang
| Challenge: | Existing methods to extract multimedia events from video and text are limited to video and images. |
| Approach: | They propose a task to jointly extract events from video and text documents . they propose 'self-supervised' cross-modal event coreference model and cross-mod transformer architecture . |
| Outcome: | The proposed method achieves 6.0% and 5.8% absolute F-score gain on video-article pairs . the proposed method can resolve coreference and extract multimodal event frames more accurately than existing methods. |
Weakly-Supervised Temporal Article Grounding (2022.emnlp-main)
Copied to clipboard
Long Chen, Yulei Niu, Brian Chen, Xudong Lin, Guangxing Han, Christopher Thomas, Hammad Ayyubi, Heng Ji, Shih-Fu Chang
| Challenge: | Existing VG models make unrealistic assumptions about how to ground video segments . a recent study has shown that video grounding can be useful for downstream applications . |
| Approach: | They propose a new task: Weakly-Supervised temporal Article Grounding (WSAG) given an article and a relevant video, WSAG aims to localize all "groundable" sentences to the video. |
| Outcome: | The proposed method is simple but effective, and it can be used in real-world applications. |
Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics (2022.findings-emnlp)
Copied to clipboard
Hyundong Cho, Chinnadhurai Sankar, Christopher Lin, Kaushik Sadagopan, Shahin Shayandeh, Asli Celikyilmaz, Jonathan May, Ahmad Beirami
| Challenge: | Recent studies have revealed the vulnerability of dialogue state tracking models to distributional shifts, resulting in poor performance. |
| Approach: | They present a toolkit for standardized and comprehensive dialogue state tracking diagnoses that provides a richer summary of strengths and weaknesses. |
| Outcome: | The proposed toolkit shows that different classes of DST models have clear strengths and weaknesses, while generation models are more promising for handling language variety and span-based classification models are robust to unseen entities. |
Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face (2023.emnlp-demo)
Copied to clipboard
Christopher Akiki, Odunayo Ogundepo, Aleksandra Piktus, Xinyu Zhang, Akintunde Oladipo, Jimmy Lin, Martin Potthast
| Challenge: | a toolkit for reproducible information retrieval research is available for free. |
| Approach: | They present a tool that integrates Pyserini and Hugging Face to enable the seamless construction and deployment of interactive search engines. |
| Outcome: | The proposed tool makes state-of-the-art retrieval models more accessible to non-IR practitioners while minimizing deployment effort. |
IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction (2024.acl-long)
Copied to clipboard
| Challenge: | Various communication frameworks assist individuals in conducting difficult conversations by providing a set of skills to apply. |
| Approach: | They propose a language-based simulation system that provides just-in-time feedback to support the practice and learning of interpersonal effectiveness skills. |
| Outcome: | The proposed training system improves self-efficacy and reduces negative emotions by 27% compared to the GPT-4 training system. |
Constrained Language Models Yield Few-Shot Semantic Parsers (2021.emnlp-main)
Copied to clipboard
Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, Benjamin Van Durme
| Challenge: | Large pretrained language models excel at generating natural language, but they are not efficient for task specific semantic parsing. |
| Approach: | They propose to use large pretrained language models as few-shot semantic parsers . they paraphrase inputs into a controlled sublanguage resembling English . |
| Outcome: | The proposed model can generate surprisingly accurate models on multiple tasks with minimal code and data. |
GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration (2023.acl-demo)
Copied to clipboard
Aleksandra Piktus, Odunayo Ogundepo, Christopher Akiki, Akintunde Oladipo, Xinyu Zhang, Hailey Schoelkopf, Stella Biderman, Martin Potthast, Jimmy Lin
| Challenge: | Using the mature and well-tested methods from the domain of Information Retrieval (IR) we propose to integrate Pyserini with Hugging Face to provide qualitative analysis tools for NLP researchers. |
| Approach: | They propose to integrate Pyserini with Hugging Face to provide qualitative analysis tools for NLP researchers. |
| Outcome: | The proposed tools can be integrated with the Hugging Face ecosystem of open-source AI libraries and artifacts. |