Papers with end-to-end fashion
Enhanced Transformer Model for Data-to-Text Generation (D19-56)
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| Challenge: | Neural models have shown significant progress on data-to-text generation tasks . data- to-text models generate descriptive texts from non-linguistic structured data . |
| Approach: | They propose a new data-to-text generation model which learns content selection and summary generation in an end-to end fashion. |
| Outcome: | The proposed model outperforms current state-of-the-art models on content selection precision and content ordering metrics. |
Coherent and Concise Radiology Report Generation via Context Specific Image Representations and Orthogonal Sentence States (2021.naacl-industry)
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| Challenge: | Neural models for text generation are often designed in an end-to-end fashion, limiting their practical usability in downstream applications. |
| Approach: | They propose a method to compute image representations specific to each sentential context and exploiting diverse sentence states to ensure topical continuity and content diversity of generated radiology reports. |
| Outcome: | The proposed method outperforms baselines on objective metrics and human evaluations by 18% and 29% respectively in the evaluation for informativeness and content ordering respectively. |
How Do Large Language Models Perform in Dynamical System Modeling (2025.findings-naacl)
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| Challenge: | Recent data-driven methods often use graph neural networks (GNNs) to learn interactions between objects. |
| Approach: | They propose prompting techniques for dynamical system modeling and evaluate their performance . they find that large language models demonstrate competitive performance without training . |
| Outcome: | The proposed methods show competitive performance without training compared to state-of-the-art methods in dynamical system modeling. |
Truth-Conditional Captions for Time Series Data (2021.emnlp-main)
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| Challenge: | Existing models with attention mechanisms can generate fluent descriptions of salient patterns in time series, but they often generate factually incorrect descriptions. |
| Approach: | They propose a model which first runs small learned programs on the input time series, then identifies the programs/patterns which hold true for the given input, and finally conditions on *only* the chosen valid program to generate the output text description. |
| Outcome: | The proposed model extracts high-level patterns from the data and generates high precision captions even though it is built on a small space of modules. |
TIMBERT: Toponym Identifier For The Medical Domain Based on BERT (2020.coling-main)
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| Challenge: | Phylogeographers have long linked the geographical spread of viruses to the growth in global tourism and international trade of goods. |
| Approach: | They propose to automate the process of place name detection in the medical domain by learning the mapping from an input sentence to the associated sentence labeled with toponyms. |
| Outcome: | The proposed model achieves an F1 score of 90.85%, compared to the state-of-the-art of 89.13%. |
SQUIRE: A Sequence-to-sequence Framework for Multi-hop Knowledge Graph Reasoning (2022.emnlp-main)
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| Challenge: | Existing methods for multi-hop knowledge graph reasoning suffer from slow and poor convergence . a transformer model can be used to learn and predict in an end-to-end fashion, giving faster convergence compared to previous methods . |
| Approach: | They propose a Sequence-to-sequence based multi-hop reasoning framework . it uses an encoder-decoder transformer structure to translate the query to a path . |
| Outcome: | The proposed framework can learn and predict in an end-to-end fashion, which gives better and faster convergence. |
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. |
Robust Retrieval Augmented Generation for Zero-shot Slot Filling (2021.emnlp-main)
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| Challenge: | Automating high quality knowledge graphs from a given collection of documents remains a challenging problem in AI. |
| Approach: | They propose a novel approach to slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. |
| Outcome: | The proposed model improves on both T-REx and zsRE slot filling datasets and ranks at the top-1 position in the KILT leaderboard. |
Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding (2025.naacl-long)
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| Challenge: | Flowcharts are typically presented as images, driving the trend of using vision-language models for end-to-end flowchart understanding. |
| Approach: | They propose a vision-language model (VLM) that generates textual representations from flowchart images and a textual Reasoner that performs question-answering based on the text representations. |
| Outcome: | Experiments on the FlowVQA and FlowLearn benchmarks demonstrate TextFlow’s state-of-the-art performance as well as its robustness. |
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction (2022.acl-long)
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| Challenge: | Existing methods to extract aspect triplets ignore the relationships between words . Enhanced Multi-Channel Graph Convolutional Network model can be used to learn relation-aware node representations. |
| Approach: | They propose an Enhanced Multi-Channel Graph Convolutional Network model to fully utilize the relations between words for ASTE task. |
| Outcome: | The proposed model outperforms state-of-the-art methods significantly on a benchmark dataset. |
Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction (2020.findings-emnlp)
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| Challenge: | Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims to extract aspect terms and opinion terms from review text in the form of opinion pairs or opinion triplets. |
| Approach: | They propose a grid-based AFOE tagging scheme to address the task in an end-to-end fashion only with one unified grid tracking task. |
| Outcome: | The proposed tagging scheme outperforms baselines and achieves state-of-the-art performance. |
CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking (2022.naacl-main)
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| Challenge: | CHEF dataset provides evidence retrieval over non-English claims . e-fact-checking is a time-consuming task, which can take journalists several hours or days. |
| Approach: | They construct a dataset of 10K real-world claims that is based on annotated evidence retrieved from the Internet. |
| Outcome: | The proposed dataset provides evidence retrieval as a latent variable and can be used to train and reason over non-English claims. |
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion (D18-1)
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| Challenge: | Existing approaches to learn orthogonal matrix aligning bilingual lexicons are suboptimal . resulting models suffer from "hubness problem" because word vectors tend to be nearest neighbors of abnormally high number of other words. |
| Approach: | They propose a unified formulation that directly optimizes a retrieval criterion in an end-to-end fashion. |
| Outcome: | The proposed approach outperforms the state-of-the-art on word translation on standard benchmarks. |
Boundary-Driven Table-Filling for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| Challenge: | Existing work focuses on extracting aspect terms and opinion terms without considering the relations between aspect terms . |
| Approach: | They propose a task to extract aspect terms, opinion terms, and expressed sentiments from a two-dimensional (2D) table. |
| Outcome: | The proposed method achieves state-of-the-art on several public benchmarks and is well-suited to the ASTE task. |
Dynamic Position Encoding for Transformers (2022.coling-1)
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| Challenge: | In neural machine translation, the general task of translating is to reduce the input sentence into smaller units (also known as statistical phrases), select an optimal translation for each unit, and place them in the correct order. |
| Approach: | They propose a novel architecture that relies on a feed-forward backbone and self-attention mechanism to encode sequential/positional information. |
| Outcome: | The proposed architecture improves on multiple datasets in French, Italian, and German and shows that it is more efficient than the current model. |
Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs (2022.emnlp-main)
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| Challenge: | a framework for language understanding models to track and improve beliefs through intermediate points in text is needed . breakpoint modeling is an efficient and end-to-end learning approach that trains models to train beliefs . understanding the behavior of models remains a formidable challenge for model safety, authors say . |
| Approach: | They propose a framework that trains models to track beliefs through intermediate points in text . their framework allows for efficient and robust learning of this type of model . |
| Outcome: | The proposed model outperforms strong representation learning approaches on a variety of NLU tasks. |
A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (2022.emnlp-main)
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| Challenge: | Argument mining (AM) is a challenging task as it requires recognizing complex argumentation structures involving multiple subtasks. |
| Approach: | They propose a generative framework where expected outputs of AM are framed as a simple target sequence. |
| Outcome: | The proposed framework achieves state-of-the-art on two AM benchmarks. |
A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check (2023.findings-emnlp)
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| Challenge: | Recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks. |
| Approach: | They propose to decompose Chinese Spelling Check into detection, reasoning, and searching subtasks and to train a module that is compatible with existing CSC models. |
| Outcome: | The proposed module can be trained for one model and benefit other models. |
Large Corpus of Czech Parliament Plenary Hearings (2020.lrec-1)
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| Challenge: | a corpus of Czech parliament plenary sessions is a valuable resource for future research . only a few public datasets are available in the Czech language . end-to-end approaches require extensive training data to produce competitive results . |
| Approach: | They present a corpus of Czech parliament plenary sessions which is a large corpus . they combine a traditional approach with a more traditional approach . |
| Outcome: | The proposed model architectures can be used to train and evaluate speech recognition systems on a large corpus of speech data and transcripts. |
Beyond the Context Window: Scaling Agentic RL via End-to-end Optimized Context Compression (2026.acl-long)
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| Challenge: | Existing reinforcement learning pipelines suffer from degraded instruction following, excessive rollout costs, and strict context limits. |
| Approach: | They propose a reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use where context length quickly becomes a bottleneck. |
| Outcome: | The proposed framework improves the success rate while maintaining the same or even lower working context length compared to baselines. |