Papers with DT
From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing sentiment analysis models lack temporal information to capture semantics of long texts. |
| Approach: | They propose a framework to exploit task-related discourse structures for sentiment analysis. |
| Outcome: | The proposed framework improves the performance even beyond existing approaches based on human annotated data. |
End-to-End Multilingual Automatic Dubbing via Duration-based Translation with Large Language Models (2025.emnlp-demos)
Copied to clipboard
| Challenge: | Automatic dubbing (AD) aims to replace the original speech with translated speech that maintains precise temporal alignment (isochrony). |
| Approach: | They propose an end-to-end automatic dubbing framework that leverages large language models to integrate translation and timing control seamlessly. |
| Outcome: | The proposed framework achieves up to 24% relative gains on English, Spanish, and Korean language pairs while maintaining competitive translation quality measured by COMET scores. |
AutoAnoEval: Semantic-Aware Model Selection via Tree-Guided LLM Reasoning for Tabular Anomaly Detection (2026.findings-eacl)
Copied to clipboard
Suhee Yoon, Sanghyu Yoon, Ye Seul Sim, Seungdong Yoa, Dongmin Kim, Soonyoung Lee, Hankook Lee, Woohyung Lim
| Challenge: | Existing approaches to tabular anomaly detection fail to reflect domain specific nature of real-world anomalies. |
| Approach: | They propose a framework that constructs pseudo-evaluation sets with semantically grounded synthetic anomalies. |
| Outcome: | The proposed framework generates pseudo-evaluation sets with semantically grounded synthetic anomalies. |
Predicting Discourse Structure using Distant Supervision from Sentiment (D19-1)
Copied to clipboard
| Challenge: | Discourse parsing is a fundamental NLP task known to enhance key downstream tasks, such as sentiment analysis, text classification and summarization. |
| Approach: | They propose a method that uses document supervision to generate abundant data for RST-style discourse structure prediction by using an optimal CKY-style tree generation algorithm. |
| Outcome: | The proposed approach performs well on the more difficult task of inter-domain discourse structure prediction, but it does not match the performance of a parser trained and tested on the same dataset. |
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
Copied to clipboard
| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
| Approach: | They propose a discourse parser that incorporates recent contextual language models to improve the performance of RST-based discourse parses. |
| Outcome: | The proposed parser outperforms existing models on two key RST datasets and on large-scale "silver-standard" discourse treebank MEGA-DT. |
Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies of document translation and query translation are outdated and do not reflect the current advances in machine translation. |
| Approach: | They compare document translation and query translation approaches to cross-lingual information retrieval . they exploit Statistical Machine Translation and Neural Machine Translation paradigms to translate queries into English and English . |
| Outcome: | The proposed approach outperforms the DT approach in translation quality and retrieval quality. |