Papers with deletion
How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior (2026.acl-long)
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Zidi Xiong, Yuping Lin, Wenya Xie, Pengfei He, Zirui Liu, Jiliang Tang, Himabindu Lakkaraju, Zhen Xiang
| Challenge: | In practice, memory designs vary widely across agents due to their diverse objectives and functionalities. |
| Approach: | They conduct an empirical study on how memory management choices impact the LLM agents’ behavior, especially their long-term performance. |
| Outcome: | The proposed methods show that LLM agents display an experience-following property, which results in highly similar agent outputs. |
MMPE: A Multi-Modal Interface using Handwriting, Touch Reordering, and Speech Commands for Post-Editing Machine Translation (2020.acl-demos)
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Nico Herbig, Santanu Pal, Tim Düwel, Kalliopi Meladaki, Mahsa Monshizadeh, Vladislav Hnatovskiy, Antonio Krüger, Josef van Genabith
| Challenge: | a shift from traditional translation to post-editing (PE) of machine-translated text can save time and reduce errors, but it also affects the design of translation interfaces. |
| Approach: | They propose a prototype that combines traditional input modes with pen, touch, and speech modalities for post-editing of machine-translated (MT) they propose to use these modalités to cross out or hand-write new text, drag and drop words for reordering, or use spoken commands to update the text in place. |
| Outcome: | The proposed interfaces can be used to cross out or hand-write new text, drag and drop words for reordering, or use spoken commands to update the text in place. |
DABERT: Dual Attention Enhanced BERT for Semantic Matching (2022.coling-1)
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| Challenge: | Existing models for semantic sentence matching lack the ability to capture subtle differences. |
| Approach: | They propose to use a Transformer-based pre-trained language model to capture fine-grained differences in sentence pairs by introducing a dual attention module and a fusion module to learn the aggregation of difference and affinity features. |
| Outcome: | The proposed method is able to capture fine-grained differences in sentence pairs. |
MMPE: A Multi-Modal Interface for Post-Editing Machine Translation (2020.acl-main)
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Nico Herbig, Tim Düwel, Santanu Pal, Kalliopi Meladaki, Mahsa Monshizadeh, Antonio Krüger, Josef van Genabith
| Challenge: | Current advances in machine translation (MT) increase the need for translators to switch from traditional translation to post-editing (PE) of machine-translated text. |
| Approach: | They propose to combine traditional input modes with pen, touch, and speech modalities for post-editing of machine-translated text. |
| Outcome: | The proposed interfaces are designed to reduce errors and save time. |
Controllable Text Simplification with Explicit Paraphrasing (2021.naacl-main)
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| Challenge: | Existing text simplification systems rely on deletion and do not paraphrase well. |
| Approach: | They propose a hybrid approach that leverages linguistically-motivated rules for splitting and deletion and couples them with a neural paraphrasing model to produce varied rewriting styles. |
| Outcome: | The proposed model improves paraphrasing capability and paraphrases more often than existing models. |
TNT: Text Normalization based Pre-training of Transformers for Content Moderation (2020.emnlp-main)
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| Challenge: | Language model pre-training (self-supervised or unsupervised learning) has been widely used in a multitude of language processing tasks such as named entity recognition, sentiment analysis, question answering and content moderation. |
| Approach: | They propose a new language pre-training model TNT for content moderation that uses a combination of masking strategy and text normalization to learn from text. |
| Outcome: | The proposed model outperforms baselines on hate speech classification task and is a potential approach to misspelling correction. |
Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction (2021.acl-long)
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| Challenge: | Experimental results demonstrate the effectiveness of Tail-to-Tail (TtT) non-autoregressive sequence prediction for Chinese Grammatical Error Correction (CGEC) |
| Approach: | They propose a framework for Chinese Grammatical Error Correction (CGEC) that uses a BERT-initialized Transformer Encoder to model the error positions. |
| Outcome: | The proposed framework solves the problem of Chinese Grammatical Error Correction (CGEC) by modeling the token dependencies. |
Mid-Air Hand Gestures for Post-Editing of Machine Translation (2021.acl-long)
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| Challenge: | In a well-connected world, translation is of everincreasing importance. |
| Approach: | They propose to use mid-air hand gestures in combination with the keyboard for editing in machine translation and post-editing workflows to improve quality. |
| Outcome: | The proposed prototype supports mid-air hand gestures for cursor placement, text selection, deletion, and reordering. |
Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks (2026.findings-acl)
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| Challenge: | Existing approaches to managing working memory are based on external mechanisms that lack awareness of the agent’s reasoning state, leading to suboptimal decisions. |
| Approach: | They propose a framework that treats working memory management as learnable policy actions and enables joint optimization of information retention and task performance through end-to-end reinforcement learning. |
| Outcome: | The proposed framework matches models 16 larger while reducing average context length by 51%, with learned strategies that adapt to model capabilities and generalize across task complexities. |
Measuring Cross-Text Cohesion for Segmentation Similarity Scoring (2024.lrec-main)
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| Challenge: | a new segmentation similarity metric is being developed for text segmentation . current metrics are content-agnostic and do not provide fine-grained scoring . |
| Approach: | They propose a word-embedding-based metric of cross-textual cohesion based on the formal linguistic definition of cohetion and incorporate it into a new segmentation similarity metric, SED. |
| Outcome: | The proposed metric provides fine-grained segmentation similarity scoring for 3 basic errors, avoiding limitations of traditional metrics. |
Meta-Evaluation of Sentence Simplification Metrics (2024.lrec-main)
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| Challenge: | Automatic Text Simplification (ATS) is a major natural language processing task that aims to help people understand complex text. |
| Approach: | They propose to use a human-annotated dataset to study automatic text simplification models to determine which metrics to use when evaluating new models. |
| Outcome: | The proposed models reconstruct the text into a simpler format by deletion, substitution, addition or splitting, while preserving the original meaning and correct grammar. |
STAGE: Simple Text Data Augmentation by Graph Exploration (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) are widely used for various tasks, but fine-tuning them requires sufficient data. |
| Approach: | They propose a method for data augmentation that utilizes a word-relation graph to select optimal words for each modification. |
| Outcome: | The proposed method is highly effective across diverse datasets and different PLMs. |