Papers with learning objective
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. |
Pre-Training Transformers as Energy-Based Cloze Models (2020.emnlp-main)
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| Challenge: | elucidates close connection between cloze modeling and representation learning over text. |
| Approach: | They propose an energy-based cloze model for representation learning over text . they assign a scalar energy score to each input token indicating how likely it is given context . |
| Outcome: | The proposed model performs better than masked language models and faster than cloze models. |
XTR meets ColBERTv2: Adding ColBERTv2 Optimizations to XTR (2025.coling-industry)
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| Challenge: | XTR eliminates the need for multi-stage retrieval, but doesn't incorporate efficiency optimizations from ColBERTv2 which improve indexing and retrieval speed. |
| Approach: | They propose a multi-vector retrieval method that simplifies retrieval into a single stage through a modified learning objective. |
| Outcome: | The proposed method eliminates the need for multistage retrieval but doesn't incorporate efficiency optimizations from ColBERTv2 which improve indexing and retrieval speed. |
Meta-Learning for Domain Generalization in Semantic Parsing (2021.naacl-main)
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| Challenge: | Existing approaches to parsing use standard supervised learning, but little attention has been given to domain generalization. |
| Approach: | They propose a meta-learning framework which targets zero-shot domain generalization for semantic parsing. |
| Outcome: | The proposed framework significantly boosts parser performance on English and Chinese spider datasets. |
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling (2022.coling-1)
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| Challenge: | a conversational system can learn to rank response candidates for a given dialogue context by computing similarity between their vector representations. |
| Approach: | They propose a framework that incorporates augmented dialogue contexts into the learning objective. |
| Outcome: | The proposed framework outperforms existing methods and is more robust to perturbations seen during inference. |
XDBERT: Distilling Visual Information to BERT from Cross-Modal Systems to Improve Language Understanding (2022.acl-short)
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| Challenge: | XDBERT (cross-modal distilled BERT) outperforms pretrained-BERT in general language understanding evaluation (GLUE), situations with adversarial generations (SWAG) benchmarks, and readability benchmarks. |
| Approach: | They propose to distill visual information from pretrained multimodal transformers to pretrained language encoders to cater to the language-heavy characteristics of NLU. |
| Outcome: | The proposed framework outperforms pretrained-BERT in general language understanding evaluation (GLUE), situations with adversarial generations (SWAG), and readability benchmarks. |
Zero-Shot Text Classification via Self-Supervised Tuning (2023.findings-acl)
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| Challenge: | Existing solutions to zero-shot text classification use pre-trained language models or large-scale annotated data. |
| Approach: | They propose a self-supervised learning paradigm to solve zero-shot text classification tasks by tuning the language models with unlabeled data. |
| Outcome: | The proposed model outperforms the state-of-the-art models on 7 out of 10 tasks and is less sensitive to prompt design. |
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding (2021.eacl-main)
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| Challenge: | Existing methods for embedding entities and relations in knowledge graphs are heuristically motivated and theoretical understanding of such embeddables is underdeveloped. |
| Approach: | They extend the random walk model of word embeddings to Knowledge Graph Embeddings (KGEs) they propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph. |
| Outcome: | The proposed learning objective is motivated by the theoretical analysis to learn KGEs from a given knowledge graph. |
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization (2021.acl-short)
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| Challenge: | Experimental results show that SimCLS can improve existing top-performing models by a large margin. |
| Approach: | They propose a framework for abstractive summarization that is conceptually simple and empirically powerful. |
| Outcome: | The proposed framework improves the performance of top-performing models by a large margin against existing top-scoring systems. |
An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction (2020.emnlp-main)
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| Challenge: | Existing methods to condition models on a concise rationale are less accurate than models that can use the entire context. |
| Approach: | They propose a method to optimize a bound on the Information Bottleneck objective to extract concise rationales from a binary mask and an end-task predictor that uses only the residual sentences. |
| Outcome: | The proposed model outperforms existing norm-minimization techniques in task performance and agreement with human rationales in the ERASER benchmark. |
Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)
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| Challenge: | named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities . |
| Approach: | They propose a set of sentence-level resampling methods to reduce data imbalance . they use a training sentence to compute the importance of each training sentence based on its tokens and entities . |
| Outcome: | The proposed methods outperform sub-sentence-level resampling, data augmentation, and loss functions on multiple corpora. |
Don’t Tell the Answer, Truly Guide the Reasoning During RL Rollouts (2026.findings-acl)
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Xinyi Wang, Jinyi Han, Zishang Jiang, Tingyun li, Jiaqing Liang, Sihang Jiang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao
| Challenge: | Existing methods such as GRPO often break down when task difficulty exceeds the model’s capacity, resulting in sparse rewards and inefficient training. |
| Approach: | They propose to measure the compatibility between external guidance and a model's intrinsic policy by introducing an adaptive framework to enhance reasoning performance while explicitly preserving high Affinity. |
| Outcome: | The proposed framework outperforms baseline models while maintaining high Affinity. |
Efficient Constituency Parsing by Pointing (2020.acl-main)
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| Challenge: | Constituency parsing is a core task in natural language processing (NLP) Existing methods for constituency paring are greedy transition-based or globally optimized. |
| Approach: | They propose a constituency parsing model that casts the problem into a series of pointing tasks. |
| Outcome: | The proposed model achieves 92.78 F1 without pre-trained models, which is faster than existing models. |
Subtopic-driven Multi-Document Summarization (D19-1)
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| Challenge: | Experimental results show that the proposed model outperforms state-of-the-art methods on benchmark datasets. |
| Approach: | They propose a multi-document summarization model that assumes a set of documents to be summarized is on the same topic. |
| Outcome: | The proposed model outperforms state-of-the-art methods on benchmark datasets. |
Personalized Transformer for Explainable Recommendation (2021.acl-long)
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| Challenge: | Recent years have witnessed the successful application of natural language generation. |
| Approach: | They propose a model that uses user and item IDs to predict the words in the target explanation to make personalized Transformer. |
| Outcome: | The proposed model outperforms BERT on the explainable recommendation task in terms of effectiveness and efficiency. |
A Closer Look at Parameter Contributions When Training Neural Language and Translation Models (2022.coling-1)
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| Challenge: | Neural models and Transformers have been used for almost every NLP task . however, the intrinsic dynamics of the training procedure have not been studied in depth for highly complex network architectures. |
| Approach: | They analyze the learning dynamics of neural language and translation models using Loss Change Allocation indicator . they use a standard Transformer architecture to train a model with three learning objectives . |
| Outcome: | The proposed model is based on a standard model that is used for training tasks. |
Embracing Large Language Models in Traffic Flow Forecasting (2025.findings-acl)
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| Challenge: | Existing methods to predict future traffic flows capture spatio-temporal dependencies, but they fail to adapt to test-time environmental changes. |
| Approach: | They propose to use large language models to help traffic flow forecasting by capturing spatio-temporal dependencies and using a large language model to select the most likely result. |
| Outcome: | The proposed method is based on large language models (LLMs) and an LLM-based selector. |
Direct Metric Optimization for Image Captioning through Reward-Weighted Augmented Data Utilization (2024.acl-long)
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Takumi Takada, Yuma Suzuki, Hiroki Takushima, Hayato Tanoue, Haruki Sato, Aiswariya Kumar, Hiroki Nishihara, Takayuki Hori, Kazuya Ueki
| Challenge: | Recent large-scale vision language models (VLMs) lack continuity between learning objective and performance metrics. |
| Approach: | They propose a lightweight final-metric-optimizing training method that replaces the expensive exploration process in RL with an offline, diverse text data augmentation method. |
| Outcome: | The proposed method achieves comparable performance to state-of-the-art RL method while saving hundreds of times more model forwarding iterations and greater amounts of computation time. |
Distributionally Robust Multilingual Machine Translation (2021.emnlp-main)
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| Challenge: | Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, but the data imbalance hinders it from performing uniformly across language pairs. |
| Approach: | They propose a distributionally robust optimization objective which minimizes the worst-case expected loss over the set of language pairs. |
| Outcome: | The proposed learning objective outperforms baseline methods on three sets of languages and shows that it is cost-effective and efficient. |
Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data (2026.acl-long)
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| Challenge: | Existing methods for automated feature generation rely on predefined operator libraries and do not incorporate feature semantics, limiting their ability to produce high-quality features. |
| Approach: | They propose a Memory-Augmented LLM-based Multi-Agent System (MALMAS) that decomposes the generation process into agents with distinct responsibilities. |
| Outcome: | The proposed method extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable machine learning. |
LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval (2025.emnlp-main)
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| Challenge: | Current dense retrievers struggle with queries with logical connectives, a use case that is often overlooked but important in downstream applications. |
| Approach: | They propose a logically-informed contrastive learning objective for dense retrievers that learns to respect the subset and mutually exclusive set relation between query results. |
| Outcome: | The proposed model improves retrieval performance and consistency on entity retrieval tasks. |
DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery (2023.emnlp-main)
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| Challenge: | Existing methods to learn compact cluster representations from coarsely labeled data are noisy and degrade the quality of learning. |
| Approach: | They propose a framework that encodes semantic structures of data into the embedding space . they retrieve k-nearest neighbors of a query as positive keys to capture similarities . |
| Outcome: | The proposed framework can retrieve more accurate neighbors and outperform state-of-the-art models by a large margin. |
BalanceSFT: Improving LLM Function Calling with Balanced Training Signals and Data Hardness (2026.findings-acl)
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Bingguang Hao, Zengzhuang Xu, Maolin Wang, Yuntao Wen, Yicheng Chen, Cunyin Peng, Long Chen, Xiangyu Zhao, Jinjie Gu, Chenyi Zhuang, Ji Zhang
| Challenge: | Currently, Supervised Fine-Tuning (SFT) is the prevailing method for equipping Large Language Models (LLMs) with function calling capabilities, but its effectiveness is often compromised by two challenges: 1) lengthy Chain-of-Thought (CoT) reasoning tokens dominate training signals over concise function calls in the learning objective; 2) scarcity of hard training examples. |
| Approach: | They propose a framework that uses a self-adjusted signal balancing loss and a hard data re-sampling strategy to selectively generate new, high-quality complex data guided by model errors. |
| Outcome: | The proposed framework surpasses state-of-the-art models like GPT-5 in function calling performance. |
Data Efficient RLVR via Off-Policy Influence Guidance (2026.acl-long)
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Erle Zhu, Dazhi Jiang, Yuan Wang, Xujun Li, Jiale Cheng, Yuxian Gu, Yilin Niu, Aohan Zeng, Jie Tang, Minlie Huang, Hongning Wang
| Challenge: | Existing data selection methods for RLVR are heuristic-based, lacking theoretical guarantees and generalizability. |
| Approach: | They propose an off-policy influence estimation method that approximates data influence using offline trajectories. |
| Outcome: | The proposed method reduces the computational cost of policy rollouts and improves storage and computation efficiency. |