Papers with sentence-level
SeqXGPT: Sentence-Level AI-Generated Text Detection (2023.emnlp-main)
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| Challenge: | Existing methods for sentence-level AIGT detection are weak . large language models (LLMs) can generate human-like content . |
| Approach: | They propose a sentence-level AIGT detection challenge using LLMs as log probability lists . they propose 'check' GPT' method that uses log probability list features to detect AIGT . |
| Outcome: | The proposed method surpasses baseline methods in sentence- and document-level detection challenges. |
Multimodal Quality Estimation for Machine Translation (2020.acl-main)
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| Challenge: | Existing work has only explored textual context. |
| Approach: | They propose to use visual and text modalities to explore Quality Estimation for Machine Translation and integrate them into multimodal QE frameworks. |
| Outcome: | The proposed approaches improve on sentence-level and document-level predictions using visual features extracted from images. |
HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction (2022.findings-acl)
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| Challenge: | Existing approaches of distantly supervised relation extraction (DSRE) focus on sentence-level or bag-level de-noising, neglecting the explicit interaction with cross levels. |
| Approach: | They propose a hierarchical contrastive learning framework for distantly supervised relation extraction to reduce noisy sentences. |
| Outcome: | The proposed framework outperforms baselines in various mainstream DSRE datasets. |
SenDetEX: Sentence-Level AI-Generated Text Detection for Human-AI Hybrid Content via Style and Context Fusion (2025.emnlp-main)
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| Challenge: | Text generated by Large Language Models (LLMs) now rivals human writing, raising concerns about its misuse. |
| Approach: | They propose a framework for sentence-level AI-generated text detection via style and context fusion. |
| Outcome: | The proposed framework outperforms baseline models in detection accuracy while exhibiting transferability and robustness. |
Selective Attention for Context-aware Neural Machine Translation (N19-1)
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| Challenge: | Recent work in context-aware NMT considers only a few previous sentences as context . current systems fail to achieve fluent, good quality translation for a full document . |
| Approach: | They propose a top-down approach to hierarchical attention for context-aware NMT which uses sparse attention to selectively focus on relevant sentences in the document context. |
| Outcome: | The proposed approach outperforms context-agnostic baselines and context-based baselines on English-German datasets. |
Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction (2022.acl-long)
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| Challenge: | Using a prompt-based model, we find that event argument extraction is efficient and generalized well to few-shot settings. |
| Approach: | They propose a model PAIE for event argument extraction using prompt tuning for extractive objectives. |
| Outcome: | The proposed model can extract arguments with the same role instead of heuristic threshold tuning. |
Substance over Style: Document-Level Targeted Content Transfer (2020.emnlp-main)
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| Challenge: | Existing language models excel at writing from scratch, but many real-world scenarios require rewriting an entire document to fit a set of constraints. |
| Approach: | They propose a document-level targeted content transfer task that addresses the challenge of rewriting an entire document coherently by generating coherent and diverse rewrites that obey a constraint while remaining close to the original document. |
| Outcome: | The proposed model outperforms existing methods by generating coherent and diverse rewrites that obey the constraint while remaining close to the original document. |
A Multi-task Learning Framework for Quality Estimation (2023.findings-acl)
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Sourabh Deoghare, Paramveer Choudhary, Diptesh Kanojia, Tharindu Ranasinghe, Pushpak Bhattacharyya, Constantin Orăsan
| Challenge: | Conventional approaches to QE involve training separate models at different levels of granularity viz., word-level, sentence-level and document-level . |
| Approach: | They propose to train a single model for sentence-level and word-level QE tasks in a multi-task learning framework and compare them to baseline models. |
| Outcome: | The proposed model improves on the single-pair, multi-patch, and zero-shot settings. |