Papers with deep learning architectures
Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual Conversational Agent Models (2020.coling-industry)
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| Challenge: | Existing voice assistant models are developed for each region or language, requiring linear effort to develop and maintain. |
| Approach: | They propose a general multilingual model framework for natural language understanding models . they show multilingual models can reach same or better performance compared to monolingual models a . |
| Outcome: | The proposed model framework can bootstrap new language models faster and reduce effort . it can reach same or better performance compared to monolingual models across language-specific test data . |
Neuro-Symbolic Natural Language Processing (2025.emnlp-tutorials)
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| Challenge: | Large Language Models (LLMs) have limitations in terms of safe and controlled reasoning, interpretability and adaptability . this tutorial aims to bridge the gap between the practical performance of LLMs and the principled modelling of language and inference of formal methods. |
| Approach: | This tutorial aims to bridge the gap between the practical performance of Large Language Models and the principled modelling of language and inference of formal methods. |
| Outcome: | This tutorial aims to bridge the gap between the performance of LLMs and the principled modelling of language and inference of formal methods. |
Recognizing UMLS Semantic Types with Deep Learning (D19-62)
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Isar Nejadgholi, Kathleen C. Fraser, Berry De Bruijn, Muqun Li, Astha LaPlante, Khaldoun Zine El Abidine
| Challenge: | Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. |
| Approach: | They propose to use general and domain-specific information to combine general and specific information to create a new entity recognition method. |
| Outcome: | The proposed method produces a state-of-the-art result on a newly released dataset, MedMentions. |
Large Scale Author Obfuscation Using Siamese Variational Auto-Encoder: The SiamAO System (2020.starsem-1)
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| Challenge: | Existing approaches to author obfuscation are largely heuristic, but they can be used to attack author identification. |
| Approach: | They propose a deep learning architecture for constructing adversarial examples against similarity-based learners and explore its application to author obfuscation. |
| Outcome: | The proposed architectures show that they can be used to attack author obfuscation . the proposed architecture shows that it can be applied to obliquacy of text . |
Speech acts and Communicative Intentions for Urgency Detection (2022.starsem-1)
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| Challenge: | Existing approaches to detect speech acts (SA) in synchronous and asynchronous dialogues have been proposed to capture communicative intentions on the part of the speaker. |
| Approach: | They propose to annotate tweets with urgency and SA and develop deep learning architectures to inject it into urgency detection. |
| Outcome: | The proposed dataset annotated for urgency and SA improves information type detection in an out-of-type configuration where models are evaluated in unseen event types during training. |
DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learning (2021.emnlp-demo)
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| Challenge: | Current deep learning architectures are data-hungry with issues mainly in generalizability and explainability. |
| Approach: | They propose a library for the integration of domain knowledge in deep learning architectures . structure of data is expressed symbolically via graph declarations and constraints can be added to deep models . |
| Outcome: | The proposed framework simplifies programming for integration of domain knowledge in deep learning architectures while separating the knowledge representation from learning algorithms. |
Deep Neural Representations for Multiword Expressions Detection (2022.acl-srw)
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| Challenge: | Existing methods for multiword expression detection are based on sequence labeling and statistical measures. |
| Approach: | They propose a weakly supervised method for multiword expressions extraction . they use a lexicon of English multiword lexical units as a reference knowledge base . |
| Outcome: | The proposed method can be easily applied to other languages. |
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms (P18-1)
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Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin
| Challenge: | Existing deep learning architectures to model compositionality in text sequences require a large number of parameters and expensive computations. |
| Approach: | They propose two additional pooling strategies over word embeddings for improved interpretability and hierarchical pooling for spatial (n-gram) information within text sequences. |
| Outcome: | The proposed pooling strategies improve interpretability and preserve spatial (n-gram) information within text sequences. |
A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting (2026.acl-industry)
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| Challenge: | Recent industrial credit scoring models rely heavily on manually tuned statistical learning methods due to the complexity of heterogeneous financial data and the challenge of modeling evolving creditworthiness. |
| Approach: | They propose a framework that reformulates credit scoring as a multi-scale sequential learning problem. |
| Outcome: | FinLangNet improves KS and bad debt rate by 6.3 pp in real world deployments. |
“Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space (2021.eacl-main)
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| Challenge: | Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. |
| Approach: | They propose to use a corpus annotated with argumentative moves and sarcasm to model sarcastic relationships using deep learning architectures. |
| Outcome: | The proposed setup improves the argumentative relation classification task using deep learning architectures. |
Weak Supervision for Learning Discourse Structure (D19-1)
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| Challenge: | a weak supervision approach is a promising tool for learning discourse structure for multi-party dialogue. |
| Approach: | They propose a data programming paradigm that allows a user to label training data using expert-composed heuristics and transform them into probability distributions of the class labels. |
| Outcome: | The proposed approach outperforms both deep learning and traditional ML approaches on the task of learning discourse structure for multi-party dialogue. |
Neural Natural Logic Inference for Interpretable Question Answering (2021.emnlp-main)
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| Challenge: | Existing question answering models are based on textual entailment tasks . prior work has focused on QA on premise-based questions . |
| Approach: | They propose a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures towards developing effective question answering models. |
| Outcome: | The proposed model outperforms previous work on multiple-choice science questions . it integrates natural logic reasoning within deep learning architectures to build proof paths . |
Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yorùbá and Twi (2020.lrec-1)
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| Challenge: | a recent study shows that word embeddings can be useful for training downstream natural language processing tasks. |
| Approach: | They compare word embeddings obtained by word embeds from curated corpora with a language-dependent processing. |
| Outcome: | The proposed model compares word embeddings with word embeds from curated corpora and a language-dependent processing on two African languages. |
LLMs as a synthesis between symbolic and distributed approaches to language (2025.findings-emnlp)
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| Challenge: | a fierce battle is being fought between symbolic and distributed approaches to language and cognition . a recent study shows that morphosyntactic knowledge is encoded in a near-discrete fashion in LLMs . |
| Approach: | a new position paper examines the role of distributed and distributed approaches in language learning . authors argue that deep learning models represent a synthesis between the two traditions . |
| Outcome: | a new position paper shows that deep learning models for language represent a synthesis between the two traditions. |
The Unstoppable Rise of Computational Linguistics in Deep Learning (2020.acl-main)
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| Challenge: | a quarter century ago, linguists assumed that language knowledge needed to be innate . but vector-space representations and machine learning algorithms are much more powerful than was thought . |
| Approach: | They trace the history of neural networks applied to natural language understanding tasks . they argue that Transformer is not a sequence model but an induced-structure model . |
| Outcome: | The proposed model is not a sequence model but an induced-structure model, the authors argue . they argue that the nature of language has had a profound impact on progress in machine learning . |
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering (2021.emnlp-main)
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| Challenge: | Existing methods to improve Question Answering performance on non-English data are expensive and limited to evaluation set. |
| Approach: | They propose a method to improve Question Answering performance without additional annotations by leveraging Question Generation models to produce synthetic samples in a cross-lingual fashion. |
| Outcome: | The proposed method outperforms baselines on four datasets in English significantly . the proposed model outperformed baselines in english and is comparable to the validation set of the original SQuAD. |
Continual Lifelong Learning in Natural Language Processing: A Survey (2020.coling-main)
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| Challenge: | Existing approaches to continual learning (CL) are costly and time-consuming. |
| Approach: | They propose to examine the problem of continual learning in NLP through the lens of various NLP tasks and provide a critical review of existing methods. |
| Outcome: | The proposed methods are critical to the development of CL models and provide a critical review of existing methods and datasets. |
Towards Explainable Hate Speech Detection (2025.findings-acl)
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| Challenge: | Recent advances in deep learning have significantly enhanced the efficiency and accuracy of natural language processing (NLP) tasks. |
| Approach: | They propose a model that uses valence, arousal, and dominance (VAD) scores to detect hate speech and a weighted sum of valent, valance, and valency (VA) scores for classification. |
| Outcome: | The proposed model can compete with state-of-the-art models in detecting hate speech and non-hate speech words based on their individual and summed VAD-values. |
HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid (2024.findings-emnlp)
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Hemank Lamba, Anton Abilov, Ke Zhang, Elizabeth Olson, Henry Dambanemuya, João Bárcia, David Batista, Christina Wille, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Humanitarian organizations can analyze data to discover trends, gather aggregated insights, manage security risks, and inform advocacy and funding proposals. |
| Approach: | They present a dataset comprising news articles in three languages containing instances of different types of violent incidents categorized by the humanitarian sector they impact. |
| Outcome: | The proposed framework can be used to identify violent incidents and identify their impact on humanitarian operations. |
Mapping the Circumplex of Affect: Geometric Analysis of Emotion Representations via Hyperspherical Contrastive Learning (2026.acl-long)
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| Challenge: | Existing methods to induce circular emotion representations in language models are limited . elucidates trade-offs involved in applying circumplex models to deep learning architectures . |
| Approach: | They propose a method to induce circular emotion representations within language models via contrastive learning on a hypersphere. |
| Outcome: | The proposed method underperforms in high-dimensional settings and fine-grained classification. |
SciDMT: A Large-Scale Corpus for Detecting Scientific Mentions (2024.lrec-main)
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| Challenge: | SciDMT is an enhanced and expanded corpus for scientific mention detection . existing corpora are limited by their small volume and entity linking capabilities . |
| Approach: | They propose to enhance SciDMT, an annotated scientific corpus for scientific mention detection. |
| Outcome: | The proposed corpus is the largest for scientific entity mention detection . it is based on deep learning architectures like SciBERT and GPT-3.5 . |