Papers with automated method
Extraction of Texters’ Explicit Emotion Expressions in Crisis Conversations (2026.findings-acl)
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| Challenge: | Existing methods for extracting present and past personal emotion expressions from text-based crisis conversations are lacking in clinically relevant areas. |
| Approach: | They propose a method for extracting present and past personal emotion expressions from text-based crisis conversations and train a transformer-based model that captures contextual distinctions between true personal emotion and other mentions. |
| Outcome: | The proposed method outperforms a regex and a model trained on real conversation data and achieves an F1 score of 0.856. |
Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter (2021.acl-short)
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| Challenge: | Existing methods for labeling emotions in text are limited, but they can be useful for many tasks. |
| Approach: | They propose a method to collect texts with induced emotion and induced sentiment labels. |
| Outcome: | The proposed method can augment the data with induced emotion and induced sentiment labels. |
PRewrite: Prompt Rewriting with Reinforcement Learning (2024.acl-short)
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| Challenge: | Prompt engineering is done manually in a trial-and-error ad-hoc fashion, authors say . |
| Approach: | They propose a method to rewrite an under-optimized prompt to a more effective prompt. |
| Outcome: | The proposed method rewrites an under-optimized prompt to a more effective prompt. |
Combining Concepts and Their Translations from Structured Dictionaries of Uralic Minority Languages (L18-1)
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| Challenge: | a new method to expand the knowledge in existing dictionaries is proposed . small Uralic languages are facing a problem of limited language resources . |
| Approach: | They propose to combine conceptually divided translations from multilingual dictionaries for small Uralic languages into a single lexical entry. |
| Outcome: | The proposed method can be used to expand existing dictionaries and provide translations when adding new entries. |
Inferring Events from Time Series using Language Models (2026.acl-long)
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| Challenge: | Prior work on reasoning about time series in conjunction with natural language has largely overlooked event descriptions and focused on tasks involving just numeric data like trend analysis or anomaly detection. |
| Approach: | They propose a method for generating tasks that test a model’s ability to reason about events associated with time series data based on sports data and develop a benchmarking method. |
| Outcome: | The proposed method can infer unobserved events from time series data, even when providing minimal context. |
FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods to detect hallucinations in large language models lack nuanced understanding of their types and manifestations. |
| Approach: | They propose a taxonomy that categorizes hallucinations into six types . they propose an augmented model to detect and mitigate hallucinosity in a fine-grained manner . |
| Outcome: | The proposed model detects and mitigates hallucinations in a fine-grained manner . it significantly boosts the performance of LLMs on GSM8K and MATH benchmarks. |
CogNet: A Large-Scale Cognate Database (P19-1)
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| Challenge: | Existing cognate databases have limited practical applications for research, despite their wide coverage and limited use in lexical tasks. |
| Approach: | They introduce a new large-scale lexical database that provides cognates across languages. |
| Outcome: | The proposed database contains 3.1 million cognate pairs across 338 languages and has an accuracy of 94%. |
Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank (2024.findings-emnlp)
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| Challenge: | Typically, creating verbal cues requires extensive human effort and is quite time-consuming. |
| Approach: | They propose a method for overgenerating and ranking verbal cues by prompting large language models to generate them and ranking them according to psycholinguistic measures and takeaways from a pilot user study. |
| Outcome: | The proposed method is comparable to human-generated mnemonics in imageability, coherence, and perceived usefulness, but there remains room for improvement due to the diversity in background and preference among language learners. |
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts (2020.emnlp-main)
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| Challenge: | Pretrained language models have been successful when finetuned to downstream tasks . however, it is difficult to determine whether the knowledge that finetuning LMs contain is learned during the pretraining or the finetailing process. |
| Approach: | They propose a method to create prompts for a diverse set of tasks using a gradient-guided search. |
| Outcome: | The proposed method performs sentiment analysis and natural language inference without additional parameters and finetuning. |
Assessing Digital Language Support on a Global Scale (2022.coling-1)
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| Challenge: | a new method is being developed to assess how well each language is doing in terms of digital language support. |
| Approach: | They develop an automated method to assess how well each language is doing in terms of digital language support. |
| Outcome: | The proposed method scrapes the names of supported languages from 143 digital tools and produces an explainable model for quantifying and monitoring it on a global scale. |
Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs (2024.emnlp-main)
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| Challenge: | Existing studies focus on case-to-case retrieval using lengthy queries, which does not match real-world scenarios. |
| Approach: | They propose a method to construct query-candidate pairs and build the largest LCR dataset to date, LEAD. |
| Outcome: | Experimental results show that the method can provide ample training signals for LCR models. |
Segmentation of Complex Question Turns for Argument Mining: A Corpus-based Study in the Financial Domain (2024.lrec-main)
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| Challenge: | Earnings Conference Calls (ECCs) are a favoured domain for the study of argumentation in context and the extraction of Argumentative Discourse Units (ADUs). |
| Approach: | Earnings Conference Calls (ECCs) are favoured domain for study of argumentation in context and extraction of Argumentative Discourse Units (ADUs). |
| Outcome: | ECCs are favoured for study of argumentation in context and extraction of Argumentative Discourse Units (ADUs). |
Toward Robust Evaluation for Multilingual Grammatical Error Correction: Can Large Language Models Replace Human References? (2026.acl-long)
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| Challenge: | Prior work has shown that using aclosest-gold reference yields more accurate performance estimates, but producing such references for each system individually is costly. |
| Approach: | They propose a method for generating closest-gold references by prompting a large language model with system outputs and a standard reference-based evaluations show weak or no correlation. |
| Outcome: | The proposed method outperforms state-of-the-art models on 14 languages across 14 benchmarks. |