Papers with document-level
When and Why is Document-level Context Useful in Neural Machine Translation? (D19-65)
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| Challenge: | Recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. |
| Approach: | They extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document- level context in NMT. |
| Outcome: | The proposed model is not interpretable as utilizing the context, and a long context is not helpful for NMT. |
M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation (2024.naacl-short)
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Benjamin Hsu, Xiaoyu Liu, Huayang Li, Yoshinari Fujinuma, Maria Nadejde, Xing Niu, Ron Litman, Yair Kittenplon, Raghavendra Pappagari
| Challenge: | Document translation is a challenge for machine translation systems that focus on textual content at the sentence level, ignoring global context and visual layout structure. |
| Approach: | They propose a benchmark dataset to evaluate document-level NMT systems . they use visual cues to preserve reading order and contiguous blocks of text . |
| Outcome: | The proposed benchmarks assess document-level NMT systems on the comprehensive task of translating semi-structured documents. |
Multilingual Denoising Pre-training for Neural Machine Translation (2020.tacl-1)
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Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer
| Challenge: | Existing approaches to pre-train models focus on only English corpora, but this is not common in machine translation. |
| Approach: | They propose a sequence-to-sequence denoising auto-encoder pre-trained on monolingual corpora . they show that it produces significant performance gains across MT tasks . |
| Outcome: | The proposed model can achieve significant performance gains across a wide variety of MT tasks. |
DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)
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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
| Challenge: | Existing relation extraction methods focus on extracting intra-sentence relations for single entities. |
| Approach: | They propose a relation extraction dataset from Wikipedia and Wikidata with three features . document-level relation extraction is a task to identify relational facts between entities . |
| Outcome: | The proposed dataset is the largest human-annotated dataset for document-level RE from plain text. |
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation (2022.naacl-main)
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Yuchen Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang, Rico Sennrich, Ryan Cotterell, Mrinmaya Sachan, Ming Zhou
| Challenge: | Standard evaluation metrics, e.g., BLEU, TER and METEOR, focus on the quality of translations at the sentence level and do not consider discourse-level features. |
| Approach: | They propose to use a metric to take discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans. |
| Outcome: | The proposed metric possesses better selectivity and interpretability at the document-level, and is more sensitive to document- level nuances. |
Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction (2021.acl-short)
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| Challenge: | Document-level relation extraction (RE) is more challenging than sentence RE as it often requires reasoning over multiple sentences. |
| Approach: | They propose a method to heuristically select evidence sentences for document-level relation extraction. |
| Outcome: | The proposed method can be easily combined with BiLSTM to achieve good performance on benchmark datasets even better than fancy graph neural network based methods. |
Document-level Relation Extraction with Dual-tier Heterogeneous Graph (2020.coling-main)
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| Challenge: | Existing methods focus on extracting relations from single sentence . document-level relation extraction requires a comprehension of the whole document . |
| Approach: | They propose a graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level relation extraction. |
| Outcome: | The proposed model achieves state-of-the-art performance on two widely used datasets. |
Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Existing document-level neural machine translation methods use all context sentences in a fixed scope. |
| Approach: | They propose an approach to select dynamic context so that document-level neural machine translation models can utilize more useful selected context sentences. |
| Outcome: | The proposed approach can select adaptive context sentences for different source sentences and significantly improves translation quality over sentences in a document. |
Analyzing Challenges in Neural Machine Translation for Software Localization (2023.eacl-main)
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| Challenge: | Neural machine translation (NMT) is a new form of machine translation that reduces the post-editing time of human annotators. |
| Approach: | They propose to use a novel multilingual UI corpus collection to test NMT for user interfaces. |
| Outcome: | The proposed test set evaluates state-of-the-art methods on a UI translation task from English to German and identifies its limitations. |
What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation (2026.acl-long)
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Shaomu Tan, Dawei Zhu, Ke Tran, Michael Denkowski, Sony Trenous, Leonardo F. R. Ribeiro, Bill Byrne, Felix Hieber
| Challenge: | Large language models (LLMs) have made document-level machine translation increasingly practical, enabled by long-context modeling and strong generation quality. |
| Approach: | They propose to use document-level MT followed by segment-level refinement to find the strongest and most stable improvements across six LLMs and seven language pairs. |
| Outcome: | The proposed method outperforms error-specific prompting and evaluate-then-refine schemes in document-level translation. |
A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling (2022.emnlp-main)
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| Challenge: | Existing document-level relation extraction methods focus on fully supervised scenarios but in real-world, incomplete labeling is a common problem because the number of entity pairs grows quadratically with the number. |
| Approach: | They propose a positive-unlabeled learning framework for document-level relation extraction (RE) that uses shift and squared ranking loss positive- unlabeles (SSR-PU) learning to solve incomplete labeling problem. |
| Outcome: | The proposed framework outperforms state-of-the-art methods under fully supervised and extremely unlabeled conditions and achieves 14 F1 points over the baseline with incomplete labeling. |
Global-to-Local Neural Networks for Document-Level Relation Extraction (2020.emnlp-main)
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| Challenge: | Relation extraction (RE) aims to identify the semantic relations between named entities in text. |
| Approach: | They propose a novel relation extraction model that encodes document information in terms of entity global and local representations and context relation representations. |
| Outcome: | The proposed model achieves superior performance on two public datasets for document-level RE. |
Leveraging Discourse Rewards for Document-Level Neural Machine Translation (2020.coling-main)
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| Challenge: | Document-level machine translation models are often not trained to explicitly ensure discourse quality. |
| Approach: | They propose a method that explicitly optimizes lexical cohesion and coherence metrics by using a reinforcement learning objective. |
| Outcome: | The proposed approach improves document translations over four different languages and three translation domains while maintaining faithfulness to the reference translation. |
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (2020.coling-main)
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| Challenge: | Existing approaches to document-level relation extraction are difficult to establish direct connections between distant entity pairs. |
| Approach: | They propose a global context-enhanced Graph Convolutional Network model which captures rich global context information of entities in a document. |
| Outcome: | The proposed model captures rich global context information of entities in a document. |
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. |
PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition (2023.findings-acl)
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| Challenge: | Existing systems for Natural Language Inference (NLI) only recognize textual entailment relations on sentence-level . however, even a simple sentence often contains multiple propositions, i.e. distinct units of meaning conveyed by the sentence . |
| Approach: | They propose a system to recognize whether one text is textually entailed by another . they use a corpus of over 45K propositions annotated by human raters to study the textual entailment relation of each proposition in a sentence individually. |
| Outcome: | The proposed dataset can be used to understand the compositionality of NLI labels. |
Towards Better Document-level Relation Extraction via Iterative Inference (2022.emnlp-main)
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| Challenge: | Existing methods only consider feature information of entity pairs, but our model exploits both feature information and previous predictions of entity pair. |
| Approach: | They propose a document-level relation extraction model with iterative inference to extract relations between entities from raw texts. |
| Outcome: | The proposed model outperforms existing methods on three commonly-used datasets. |
Entity-Level Sentiment Analysis (ELSA): An Exploratory Task Survey (2022.coling-1)
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| Challenge: | Existing tasks and models for identifying sentiment expressed in text are lacking in identifying overall sentiment . prior work focused on document-level polarity classification, but ELSA is under-explored for longer texts with multiple mentions and opinions towards the same entity. |
| Approach: | They propose to use document-, sentence-, and target-level sentiment analysis to identify overall sentiment expressed towards volitional entities in a document. |
| Outcome: | The proposed task is referred to as "entity-level sentiment analysis" the proposed task performs poorly for longer texts with multiple mentions and opinions . |
Consistent Document-level Relation Extraction via Counterfactuals (2024.findings-emnlp)
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| Challenge: | Document-level relation extraction models trained on factual data exhibit inconsistent behavior, relying on spurious signals such as specific entities and external knowledge to extract triples. |
| Approach: | They propose a counterfactual data generation approach for document-level relation extraction datasets using entity replacement to generate triples from factual data. |
| Outcome: | The proposed approach extracts triples from factual data but fails on counterfactual modification. |
On Search Strategies for Document-Level Neural Machine Translation (2023.findings-acl)
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| Challenge: | Document-level neural machine translation models produce a more consistent output across a document . however, the exact decoding strategy is often not described and not mentioned at all. |
| Approach: | They propose to use standard automatic metrics and specific linguistic phenomena to compare different decoding schemes. |
| Outcome: | The proposed decoding strategies perform similar to each other on three standard document-level translation benchmarks. |
On Event Individuation for Document-Level Information Extraction (2023.findings-emnlp)
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| Challenge: | a bomb exploded in a restaurant in Lima, and a second device was deactivated by the police . |
| Approach: | They argue that the task demands definitive answers to thorny questions of *event individuation* they argue that even human experts disagree on the task . |
| Outcome: | The proposed task demands definitive answers to thorny questions of *event individuation* . the proposed task also raises concerns about the usefulness of template filling metrics . |
Non-Autoregressive Document-Level Machine Translation (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive translation models struggle with document context and handling discourse phenomena. |
| Approach: | They propose a simple but effective design of sentence alignment between source and target to improve their performance on document-level machine translation. |
| Outcome: | The proposed model achieves high acceleration on documents and sentence alignment significantly enhances their performance. |
Cross-Document Cross-Lingual NLI via RST-Enhanced Graph Fusion and Interpretability Prediction (2025.emnlp-main)
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| Challenge: | Despite the development of many subdirections, Cross-Document Cross-Lingual NLI remains largely unexplored. |
| Approach: | They propose a novel paradigm that extends traditional NLI capabilities to multi-document, multilingual scenarios by integrating RST-enhanced graph fusion with interpretability-aware prediction. |
| Outcome: | The proposed method improves on existing models and document-level NLI to multi-document, multilingual scenarios. |