Papers with two-step approach
Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing (2023.findings-eacl)
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| Challenge: | Existing approaches that model alignments between sentences fail at compositional generalization tasks, resulting in a resurgence of such approaches. |
| Approach: | They propose a two-step approach that first translates input sentences monotonically and then reorders them to obtain the correct output. |
| Outcome: | The proposed approach improves compositional generalization over existing models and other approaches that exploit gold alignment annotations. |
SEAL: Interactive Tool for Systematic Error Analysis and Labeling (2022.emnlp-demos)
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| Challenge: | Existing models that fail on tail data or rare groups are difficult to identify due to lack of explicit labels. |
| Approach: | They propose a systematic error analysis and labeling tool that uses a two-step approach to identify high-error slices of data and then give human-understandable semantics to those underperforming slices. |
| Outcome: | The proposed tool identifies high-error slices of data and gives human-understandable semantics to those underperforming slices. |
Mixed-Lingual Pre-training for Cross-lingual Summarization (2020.aacl-main)
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| Challenge: | Cross-lingual summarization (CLS) aims at producing a summary in the target language for an article in the source language. |
| Approach: | They propose a mixed-lingual pre-training scheme that leverages both cross-lingual tasks such as translation and monolingual tasks like masked language models. |
| Outcome: | The proposed model improves on the translation and masked language models with no task-specific components and saves memory. |
Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-training (2023.findings-acl)
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| Challenge: | Existing methods to train automatic text simplification systems for languages other than English are limited by the lack of parallel data. |
| Approach: | They propose to use German Easy Language as a corpus of automatic text simplification systems to fine-tune language models to the style characteristics of the language. |
| Outcome: | The proposed language models adapt to the style characteristics of Easy Language and output more accessible texts. |
A Tale of Two Scripts: Transliteration and Post-Correction for Judeo-Arabic (2026.eacl-long)
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| Challenge: | Thousands of JA texts are available online, covering genres such as philosophy, biblical commentary, and Bible translations. |
| Approach: | They propose a two-step approach to automatically transliterate Judeo-Arabic into Arabic script using simple character-level mapping followed by post-correction to address grammatical and orthographic errors. |
| Outcome: | The proposed method enables Arabic NLP tools to perform morphosyntactic tagging and machine translation, which would have been impossible on the original texts. |
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization (2022.acl-long)
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| Challenge: | Prior research on radiology report summarization has focused on single-step end-to-end models which subsume the task of salient content acquisition. |
| Approach: | They propose a two-step extractive summarization followed by abstractive summaries and a new method that breaks down the extractive part into two independent tasks: extraction of salient (1) sentences and (2) keywords. |
| Outcome: | The proposed model improves on English radiology reports with an overall improvement in F1 score of 3-4% compared to single-step and two-step-with-single-extractive-process baselines. |
D2S: Document-to-Slide Generation Via Query-Based Text Summarization (2021.naacl-main)
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| Challenge: | Existing research efforts to automate the document-to-slide generation process face a critical challenge: no publicly available dataset for training and benchmarking. |
| Approach: | They propose a dataset SciDuet that gathers papers and their corresponding slides from recent years’ NLP and ML conferences. |
| Outcome: | The proposed system outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation. |
Intent Detection in the Age of LLMs (2024.emnlp-industry)
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| Challenge: | Traditional approaches to intent detection struggle with out-of-scope (OOS) detection. |
| Approach: | They propose to use adaptive in-context learning and chain-of-thought prompting to detect intent in SOTA LLMs. |
| Outcome: | The proposed system achieves 2% of native accuracy with 50% less latency. |
Mapping probability word problems to executable representations (2021.emnlp-main)
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Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc de Raedt, Walter Daelemans
| Challenge: | a recent paper addresses the problem of solving math word problems automatically . a number of approaches have been proposed for solving word problems . |
| Approach: | They employ a sequence-to-sequence model to generate intermediate representations for word problems . they then use a probabilistic programming system to provide the answer . their best performing model incorporates general-domain contextualised word representations . |
| Outcome: | The proposed model is the best performing on a declarative language and a probabilistic programming system. |
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning (2020.findings-emnlp)
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| Challenge: | Existing work on knowledge graphs infers a missing relationship between entities with a multi-hop rule . Empirical results show that our multi-chain multi-homing (MCMH) rules yield superior results compared to the standard single-chain approaches. |
| Approach: | They propose to use a generalized form of multi-hop rules to learn generalized rules efficiently . they propose to select a small set of relation chains as a rule and evaluate confidence . |
| Outcome: | The proposed method outperforms the existing methods and the existing frameworks. |
Cross-Domain Review Generation for Aspect-Based Sentiment Analysis (2021.findings-acl)
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| Challenge: | Existing domain adaptation methods for Aspect-Based Sentiment Analysis lack finegrained labeled data. |
| Approach: | They propose a new domain adaptation paradigm called cross-domain review generation which aims to generate target-domain reviews with fine-grained annotation based on the labeled source domain. |
| Outcome: | The proposed approach is superior to state-of-the-art domain adaptation methods. |
Estimating Lexical Complexity from Document-Level Distributions (2024.lrec-main)
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| Challenge: | Existing methods for complexity estimation are limited to entire documents . health assessment tools are too short for existing methods to apply . |
| Approach: | They propose a two-step approach for estimating lexical complexity that does not rely on pre-annotated data. |
| Outcome: | The proposed method is tested on the Norwegian language and compares with other assessment tools. |
EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing approaches to build multi-way aligned corpus from bilingual data are limited by their scale. |
| Approach: | They propose to build a multi-way aligned corpus from bilingual data using two steps to extract candidate alignes and generate the final alignets from the candidates. |
| Outcome: | The proposed method improves on two publicly available datasets with +1.1 and +1.4 BLEU points. |
ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning (2024.findings-acl)
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| Challenge: | Charts provide visual representations of data and are used for analyzing information, addressing queries, and conveying insights to others. |
| Approach: | They propose a chart-specific vision-language Instruction-following dataset with 191K instructions and a pipeline model that extracts chart data tables and inputs them into a LLM. |
| Outcome: | The proposed model can solve a wide range of chart-related tasks, achieving state-of-the-art results on four tasks. |
A Two-Step Approach for Implicit Event Argument Detection (2020.acl-main)
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| Challenge: | et al., 2015) only consider local arguments in the same sentence of the event trigger. |
| Approach: | They propose to decompose the implicit event argument detection task into two sub-problems . they propose to use argument head-word detection and head-to-span expansion to reduce the number of candidates. |
| Outcome: | The proposed model achieves better performance than a strong sequence labeling baseline. |
ESPRIT: Explaining Solutions to Physical Reasoning Tasks (2020.acl-main)
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Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | Neural networks lack the ability to reason about qualitative physics and cannot generalize to scenarios and tasks unseen during training. |
| Approach: | They propose a framework for reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. |
| Outcome: | The proposed framework generates explanations of how the physical simulation will causally evolve so that an agent or a human can reason about a solution using interpretable descriptions. |
How to Solve Few-Shot Abusive Content Detection Using the Data We Actually Have (2024.lrec-main)
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| Challenge: | Existing datasets for abusive language detection are expensive and lack of knowledge about the target is a challenge. |
| Approach: | They propose to build models cheaply for a new target label set and/or language, using only a few training examples of the target domain. |
| Outcome: | The proposed model improves monolingually and across languages using existing datasets and only a few-shots of the target domain. |
Model Merging and Safety Alignment: One Bad Model Spoils the Bunch (2024.findings-emnlp)
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| Challenge: | Existing methods for merging large language models often overlook safety alignment during merging, leading to misaligned models. |
| Approach: | They propose to combine safety and domain-specific data to optimize model merging techniques . they propose to use this data to maximize model alignment . |
| Outcome: | The proposed method allows for models that excel in both domain expertise and alignment. |
Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting (2023.findings-emnlp)
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Benjamin Yan, Ruochen Liu, David Kuo, Subathra Adithan, Eduardo Reis, Stephen Kwak, Vasantha Venugopal, Chloe O’Connell, Agustina Saenz, Pranav Rajpurkar, Michael Moor
| Challenge: | Existing methods for generating reports from medical images conflate the content of the report with its style, which can lead to inaccurate reports. |
| Approach: | They propose a two-step approach to generate radiology reports from medical images using large language models and a graph representation of reports. |
| Outcome: | The proposed approach improves the performance of human evaluations with clinical raters. |
A Two-Step Approach for Data-Efficient French Pronunciation Learning (2024.emnlp-main)
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| Challenge: | Recent studies have addressed intricate phonological phenomena in French, relying on extensive linguistic knowledge or a significant amount of sentence-level pronunciation data. |
| Approach: | They propose a grapheme-to-phoneme and post-lexical processing approach to address French phonological phenomena using sentence-level pronunciation data. |
| Outcome: | The proposed approach mitigates the lack of extensive labeled data and serves as a feasible solution for addressing French phonological phenomena even under resource-constrained environments. |