Papers with text generation problem
Towards Generative Aspect-Based Sentiment Analysis (2021.acl-short)
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| Challenge: | Existing work on Aspect-based sentiment analysis ignores the rich label semantics of ABSA. |
| Approach: | They propose to tackle various ABSA tasks in a unified generative framework . they propose to use annotation-style and extraction-style modeling to enable training . |
| Outcome: | The proposed framework achieves state-of-the-art on four ABSA tasks across multiple benchmark datasets. |
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (2020.acl-main)
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| Challenge: | Existing methods for text generation ignore faithfulness between generated text and table . current methods ignore faithfulity, leading to generated information that goes beyond table content . |
| Approach: | They propose a Transformer-based generation framework to enforce faithfulness between generated text and table . they propose metric to evaluate faithfulness and automatic metric for automatic generating . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in automatic evaluations and human evaluations. |
ToXCL: A Unified Framework for Toxic Speech Detection and Explanation (2024.naacl-long)
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| Challenge: | Existing models that focus on explicit toxic speech detection and explanation are prone to error propagation problems . et al., 2018) show that toxic speech models can be prone for generating errors . |
| Approach: | They propose a framework that can detect and explain toxic speech using a target group generator and an encoder-decoder model. |
| Outcome: | The proposed model outperforms baseline models and achieves state-of-the-art effectiveness . the proposed model generates a toxic explanation that matches the ground truth explanation . |
GECSum: Generative Evaluation-Driven Sequence Level Contrastive Learning for Abstractive Summarization (2024.lrec-main)
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| Challenge: | Abstractive summarization is a technique in natural language processing that involves generating a summary of a source document by creating new sentences and phrases. |
| Approach: | They propose a sequence-level contrastive learning framework that leverages the semantic understanding capabilities of the abstractive model itself to evaluate summary in reference-based settings. |
| Outcome: | The proposed framework outperforms the state-of-the-art in four summarization datasets. |
Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments (2024.emnlp-main)
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| Challenge: | Existing work on event-specific argument extraction is limited to contiguous spans of text . Existing approaches to event-centric information extraction are limited to explicit arguments . |
| Approach: | They propose two key argument types that cannot be modeled by existing EE frameworks . implicit and scattered arguments are crucial to elicit full breadth of information required for proper event modeling. |
| Outcome: | The proposed dataset includes 7,464 argument annotations from online health discourse. |
ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs (2026.findings-acl)
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| Challenge: | Existing methods rely on text retrieval and geographic knowledge bases to generate coordinates, and they are prone to error propagation and dependency on structured knowledge bases. |
| Approach: | They propose to use large language models to convert geographic coordinates into geohash sequences and introduce a Chain-of-Thought mechanism to enhance the model’s reasoning over spatial relationships. |
| Outcome: | The proposed framework can handle explicit address queries in single-point predictions and effectively resolve vague relative location queries. |