Papers with supervised systems
Abstractive Timeline Summarization (D19-54)
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| Challenge: | Prior approaches to TLS focus on extractive methods, which generate extractive timelines . a study with human judges shows that our abstractive system also produces output that is easy to read and understand. |
| Approach: | They propose an abstractive timeline summarization system that is unsupervised . their system outperforms extractive systems in terms of ROUGE scores . |
| Outcome: | The proposed system outperforms extractive systems in terms of ROUGE scores . it produces output that is easy to read and understand, the authors say . |
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations (P18-1)
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| Challenge: | Using neural machine translation, we generate more than 50 million sentential paraphrase pairs from a large parallel corpus. |
| Approach: | They use a dataset of more than 50 million English-English sentential paraphrase pairs to generate them automatically using neural machine translation. |
| Outcome: | The proposed dataset outperforms all supervised systems on every SemEval semantic textual similarity competition and shows how it can be used for paraphrase generation. |
Template Filling with Generative Transformers (2021.naacl-main)
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| Challenge: | Template filling tasks are usually tackled by a pipeline of two separate systems, one for role-filler extraction and another for template/event recognition. |
| Approach: | They propose a framework that naturally models the dependence between entities within a single event and across multiple events described in a document. |
| Outcome: | The proposed framework outperforms pipeline-based approaches and other neural baselines that do not model between-event dependencies on documents containing multiple events. |
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings (P18-1)
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| Challenge: | Existing methods to learn cross-lingual word embeddings have failed in more realistic scenarios . a fully unsupervised initialization and a robust self-learning algorithm are needed to improve the existing methods. |
| Approach: | They propose an unsupervised initialization method that exploits structural similarity of embeddings and a robust self-learning algorithm that iteratively improves it. |
| Outcome: | The proposed method achieves the best published results in standard datasets even surpassing previous supervised systems. |
Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation (P19-1)
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| Challenge: | Recent advances in unsupervised bilingual word embeddings make it possible to mine parallel sentences from comparable corpora. |
| Approach: | They propose a strong unsupervised system for parallel sentence mining based on cosine similarities of source and target words . they show that parallel sentences mined from real-life sources improve unsupervised MT . |
| Outcome: | The proposed system improves unsupervised MT on three language pairs. |
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection (2020.emnlp-main)
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| Challenge: | Existing approaches to disfluency detection rely on human annotations, which are expensive to obtain. |
| Approach: | They propose an unsupervised learning paradigm which can work with unlabeled text corpora. |
| Outcome: | The proposed method performs better than existing supervised systems using word embeddings. |
Improving Entity Linking by Modeling Latent Relations between Mentions (P18-1)
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| Challenge: | Entity linking systems often exploit relations between textual mentions to decide if the linking decisions are compatible. |
| Approach: | They treat relations as latent variables while optimizing the neural entity-linking model without supervision. |
| Outcome: | The proposed model outperforms its relation-agnostic version and significantly outperformed its relational version. |
Boosting Entity Linking Performance by Leveraging Unlabeled Documents (P19-1)
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| Challenge: | a new approach to entity linking relies on unlabeled documents and Wikipedia . a supervised approach uses only natural information, such as unlabed documents . |
| Approach: | They propose a method which exploits only naturally occurring information . they construct a high recall list of candidate entities for each mention in an unlabeled document . |
| Outcome: | The proposed model outperforms fully-supervised state-of-the-art systems on standard test sets. |
Extractive Summarization via ChatGPT for Faithful Summary Generation (2023.findings-emnlp)
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| Challenge: | Abstractive summarization methods struggle with generating ungrammatical or even nonfactual contents. |
| Approach: | They evaluate ChatGPT's performance on extractive summarization and compare it with traditional fine-tuning methods on benchmark datasets. |
| Outcome: | The proposed pipeline performs better than abstractive methods on summary faithfulness and in-context learning. |
Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations (2024.findings-naacl)
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| Challenge: | supervised systems have not replaced dedicated supervised models for machine translation tasks. |
| Approach: | They propose to guide LLMs to post-edit MT with feedback from MQM annotations . they then fine-tune the LLM to improve its ability to exploit the feedback . |
| Outcome: | The proposed model improves TER, BLEU and COMET scores on Chinese-English, English-German and English-Russian data. |
Can we teach language models to gloss endangered languages? (2024.findings-emnlp)
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| Challenge: | Prior research has explored statistical and neural methods for automatically producing IGT. |
| Approach: | They propose to use in-context learning to generate interlinear glossed text . they propose to employ supervised learning to select examples to provide in-text . |
| Outcome: | The proposed methods beat standard transformer baselines, despite requiring no training at all. |
Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games (2023.findings-emnlp)
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| Challenge: | Text game agents are often modeled using reinforcement learning, but their performance is limited. |
| Approach: | They propose a self-supervised behavior cloning transformer for text games . they explore trajectories that lead to reward within the games and then train small models . their approach consistently uncovers generalizable training data, achieving 90% performance of supervised systems across three benchmark text games. |
| Outcome: | The proposed model achieves 90% performance on three text games. |
Unsupervised Statistical Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation (NMT) systems can be trained from monolingual corpora without supervision. |
| Approach: | They propose a phrase-based approach that trains from monolingual corpora . their method is based on phrase-driven Statistical Machine Translation (SMT) they propose to train NMT systems without supervision from monolinguistic corpors . |
| Outcome: | The proposed approach improves on the existing supervised systems by combining a phrase table with an n-gram language model and fine-tuning hyperparameters through an unsupervised MERT variant. |
A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation (2020.emnlp-main)
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| Challenge: | Existing sense embeddings fail to embed sense knowledge in semantic networks. |
| Approach: | They propose a Synset Relation-Enhanced Framework that leverages sense relations for sense embedding enhancement and a try-again mechanism that implements WSD again. |
| Outcome: | The proposed system outperforms knowledge-based systems with 20% SemCor data on all-words and lexical datasets. |
Unsupervised Morphological Paradigm Completion (2020.acl-main)
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| Challenge: | a task of generating morphological paradigms is a challenging unsupervised task for natural language processing systems . acuidados y acciones del idioma es a problem in linguistic annotators. |
| Approach: | They propose a task of unsupervised morphological paradigm completion using raw text and a lemma list. |
| Outcome: | The proposed system outperforms trivial baselines on 14 typologically diverse languages with ease and higher accuracy than minimally supervised systems. |
OpineSum: Entailment-based self-training for abstractive opinion summarization (2023.findings-acl)
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| Challenge: | Abstractive summarization is promising for fluently comparing opinions from a set of reviews about a place or product. |
| Approach: | They propose a novel method that automatically leverages common opinions across reviews to create powerful abstractive models. |
| Outcome: | The proposed method outperforms strong peer systems in both settings. |
DiZiNER: Disagreement-guided Instruction Refinement via Simulating Pilot Annotation for Zero-shot Named Entity Recognition (2026.acl-long)
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| Challenge: | Large language models have advanced information extraction (IE) by enabling zero-shot and few-shot named entity recognition (NER) but their outputs still show persistent and systematic errors. |
| Approach: | They propose a framework that simulates the pilot annotation process and employs LLMs as both annotators and supervisors to refine model disagreements. |
| Outcome: | Using a pilot annotation process, the proposed framework outperforms its supervisor model on 18 benchmarks. |
Prompting PaLM for Translation: Assessing Strategies and Performance (2023.acl-long)
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| Challenge: | Large language models trained on multilingual but not parallel text exhibit remarkable ability to translate between languages. |
| Approach: | They investigate the pathways language model which has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
| Outcome: | The pathways language model (PaLM) has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real World (2025.findings-acl)
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Sina Semnani, Pingyue Zhang, Wanyue Zhai, Haozhuo Li, Ryan Beauchamp, Trey Billing, Katayoun Kishi, Manling Li, Monica Lam
| Challenge: | Using a partially reannotated subset of the Armed Conflict Location & Event Data, we analyze 39,786 conflict events across 20 languages and 171 countries. |
| Approach: | They propose a large-scale conflict event dataset with extensive coverage of region-specific entities. |
| Outcome: | The proposed method detects event arguments and entities through holistic document understanding and normalizes them across the multilingual dataset. |