Papers with input representation
Transformers for Tabular Data Representation: A Survey of Models and Applications (2023.tacl-1)
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| Challenge: | Recent research efforts extend LMs by developing neural representations for structured data. |
| Approach: | They propose to extend transformer-based language models to tabular data by analyzing inputs, model training, and supported downstream tasks. |
| Outcome: | The proposed models are compared against existing models and are based on a traditional pipeline. |
TTM-RE: Memory-Augmented Document-Level Relation Extraction (2024.acl-long)
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| Challenge: | Existing methods for document-level relation extraction are ineffective in exploiting the full potential of large amounts of training data with varied noise levels. |
| Approach: | They propose a novel approach that integrates a trainable memory module with a noisy-robust loss function that accounts for the positive-unlabeled setting to unlock the full potential of large-scale noisy training data. |
| Outcome: | The proposed model outperforms existing methods on a ReDocRED benchmark dataset with an absolute F1 score improvement of over 3%. |
Learning Domain Representation for Multi-Domain Sentiment Classification (N18-1)
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| Challenge: | Training data for sentiment analysis is abundant in multiple domains, yet scarce for other domains. |
| Approach: | They propose to use domain-specific representations of input sentences to improve sentiment classification . they use a descriptor vector to map adversarially trained domain-general Bi-LSTM inputs into domain- specific representations . |
| Outcome: | The proposed model outperforms existing methods on multi-domain sentiment analysis significantly. |
AggGen: Ordering and Aggregating while Generating (2021.acl-long)
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| Challenge: | AggGen is a data-to-text model which re-introduces two explicit sentence planning stages into neural data- to-text systems: input ordering and input aggregation. |
| Approach: | AggGen re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input aggregation. |
| Outcome: | AggGen is a data-to-text model which re-introduces two explicit sentence planning stages into neural data- to-text systems: input ordering and input aggregation. |
Contrastive Explanations for Model Interpretability (2021.emnlp-main)
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| Challenge: | Existing methods for producing model explanations seek all causal factors at once, making them difficult to comprehend. |
| Approach: | They propose a method to produce contrastive explanations in the latent space . they use attribution and token/span attribution to produce models that consider only contrastive reasoning . |
| Outcome: | The proposed method allows model behavior to consider only contrastive reasoning . it also uncovers which aspects of the input are useful for and against particular decisions . |
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data (P19-1)
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| Challenge: | Existing methods to perform relation extraction are feature-based or kernel-based, but the results of our study show that they can improve the performance of a baseline model with more than 10% absolute increase in F1-score. |
| Approach: | They propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. |
| Outcome: | The proposed model outperforms the state-of-the-art models on ACE 2005 Chinese and English corpus and significantly improves the performance of a baseline model with more than 10% increase in F1-score. |
Recurrent Positional Embedding for Neural Machine Translation (D19-1)
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| Challenge: | Existing translation systems that use positional embeddings only encode static order dependencies based on discrete numerical information, which may hinder the improvement of translation capacity. |
| Approach: | They propose a recurrent positional embedding approach based on word vectors that are learned by a neural network and integrated into existing multi-head self-attention models. |
| Outcome: | The proposed approach improves translation performance over the state-of-the-art Transformer baseline in English-to-German and NIST Chinese-to English translation tasks. |
Frustratingly Easy Performance Improvements for Low-resource Setups: A Tale on BERT and Segment Embeddings (2022.lrec-1)
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| Challenge: | Understanding why contextualized embeddings work is still an active area of research. |
| Approach: | They propose to use a BERT architecture to encode a sub-word, position and a segment embedding as input representations for each sub- word. |
| Outcome: | The proposed model performs well on single-sentence prediction tasks while swapping segment IDs in paired-sentent tasks. |
GoT: Effective Graph-of-Thought Reasoning in Language Models (2024.findings-naacl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have been advancing at an unprecedented pace. |
| Approach: | They propose a graph-based approach which models human thought processes as a chain and as 'graphs' by representing thought units as nodes and connections between them as edges, they capture the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. |
| Outcome: | The proposed model improves on a text-only reasoning task and a multimodal reasoning task. |
MA-BERT: Learning Representation by Incorporating Multi-Attribute Knowledge in Transformers (2021.findings-acl)
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| Challenge: | Existing methods for incorporating external attribute knowledge into deep neural networks are concatenating multiple attributes to word/text representation or treating them as biases to adjust attention distribution. |
| Approach: | They propose a multi-attribute BERT to incorporate external attribute knowledge into deep neural networks. |
| Outcome: | The proposed method outperforms existing models and models on three benchmark datasets. |
Disfluency Detection using Auto-Correlational Neural Networks (D18-1)
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| Challenge: | a recent study proposes an auto-correlational neural network (ACNN) that can detect disfluency in speech . the model uses a convolutional neural system and augments it with a new auto-corrector . |
| Approach: | They propose a convolutional neural network model that captures "rough copy" dependencies . the model is based on a new auto-correlation operator that capture the kinds of "rough copies" dependency . |
| Outcome: | The proposed model outperforms the baseline CNN on a disfluency detection task with a 5% increase in f-score. |
End-to-end Parsing of Procedural Text into Flow Graphs (2024.lrec-main)
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| Challenge: | Existing flow graph parsers lack sufficient annotated data to train them . a lack of annotation can cause costly training, and poor flow graph training results in a large improvement. |
| Approach: | They propose a multi-task framework that performs tagging and graph generation simultaneously . they take advantage of the abundance of unlabelled recipes and generate noisy silver annotations . |
| Outcome: | The proposed model can unify the input representation and use compact encoders, resulting in small models with significantly fewer parameters than existing models. |
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs (2024.emnlp-main)
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| Challenge: | Recent prompting techniques have improved LLMs’ performance on various reasoning tasks, but there is little understanding of what triggers reasoning abilities in LLM in the inference stage. |
| Approach: | They propose a method that transforms a natural language problem into code and directly prompts the LLM using the generated code without resorting to external code execution. |
| Outcome: | The proposed method boosts multiple LLMs by 22.52 percentage points on GPT 3.5, 7.75 on Mixtral, and 16.78 on Mistral. |
ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget (2024.findings-acl)
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| Challenge: | Entity Linking and Relation Extraction (EL) are fundamental tasks in Natural Language Processing. |
| Approach: | They propose a Retriever-Reader architecture for Entity Linking and Relation Extraction . they propose an input representation that incorporates the candidate entities alongside the text . |
| Outcome: | The proposed architecture achieves state-of-the-art in in- and out-of domain benchmarks while using academic budget training and with 40x inference speed compared to competitors. |
Problem Solved? Information Extraction Design Space for Layout-Rich Documents using LLMs (2025.findings-emnlp)
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| Challenge: | a new open-source layout-aware IE test suite is available for download at https://github.com/gayecolakoglu/layIE-LLM. |
| Approach: | They propose an open-source layout-aware IE test suite that provides a layout-based IE pipeline. |
| Outcome: | The proposed method achieves 13.3–37.5 F1 points more than a baseline configuration using the same LLM. |
CodeComplex: Dataset for Worst-Case Time Complexity Prediction (2025.findings-emnlp)
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| Challenge: | Reasoning ability of large language models (LLMs) is crucial in complex decision-making tasks. |
| Approach: | They propose to use code time complexity prediction to assess LLMs' reasoning ability. |
| Outcome: | The proposed dataset comprises 4,900 Java codes and an equivalent number of Python codes. |
Opinions Are Not Always Positive: Debiasing Opinion Summarization with Model-Specific and Model-Agnostic Methods (2024.lrec-main)
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| Challenge: | Existing opinion summarization frameworks are reluctant to generate negative summaries given input of negative opinions. |
| Approach: | They propose to disentangle input into sentiment-relevant and sentiment-irrelevant components through adversarial loss. |
| Outcome: | The proposed approaches reduce sentiment bias in the existing opinion summarization dataset . the proposed approaches generate better summaries with a more balanced emotional polarity distribution . |
Trustworthy and Explainable Causal Representation Learning in Transformers (2026.findings-acl)
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| Challenge: | Existing approaches to interpretable representation learning rely on masks that weight the significance of input features, but the origin of these masks is uncertain. |
| Approach: | They propose a causal framework that directly learns identifiable representations from attention weights rather than relying on importance masks. |
| Outcome: | The proposed framework learns identifiable and explainable representations from attention weights, rather than masks, and guarantees faithfulness on real-world datasets. |
Weight Tying Biases Token Embeddings Towards the Output Space (2026.findings-acl)
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| Challenge: | Weight tying is a common practice in language model design, but its impact on learning embedding space remains unclear. |
| Approach: | They show that weight tying optimizes the embedding matrix for output prediction . they also show that tied embeddable matrices align more closely with output embedders . |
| Outcome: | The proposed weight tying approach harms performance at scale and has implications for training smaller LLMs. |