Papers with DNN
Layerwise Relevance Visualization in Convolutional Text Graph Classifiers (D19-53)
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| Challenge: | Existing explainability methods do not focus on intermediate states in hidden layers of Deep Neural Networks (DNNs). |
| Approach: | They propose a method that visits visible and hidden layers of a deep neural network and projects them onto the interpretable domain. |
| Outcome: | The proposed method yields meaningful layerwise explanations for a GCN sentence classifier. |
SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics (2021.findings-acl)
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| Challenge: | Existing models have limitations to generalize to diverse semantic phenomena, and it is unclear whether they can capture compositional meanings. |
| Approach: | They propose a systematic generalization testbed based on Natural language semantics to map natural language sentences to multiple meaning representations. |
| Outcome: | The proposed model can generalize to unseen combinations of quantifiers, negations, and modifiers, but not to the others. |
Arabic Synonym BERT-based Adversarial Examples for Text Classification (2024.eacl-srw)
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| Challenge: | Often, research studies quantifying the impact of adversarial text attacks have been applied only to models trained in English. |
| Approach: | They propose a word-level study of adversarial text examples in Arabic . they use a synonym attack with a BERT model to assess their robustness . |
| Outcome: | The proposed attack compares Arabic adversarial examples with their original examples and regains 2% accuracy after training. |
A k-Nearest Neighbor Approach towards Multi-level Sequence Labeling (N19-2)
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| Challenge: | Existing methods for complex dialog management require limited training data. |
| Approach: | They propose a method for intent recognition for complex dialog management in low resource situations . they use windowed word n-grams, POS tag n grams and pre-trained word embeddings as features . |
| Outcome: | The proposed method performs better with less than 1% of the data size than existing methods but requires considerably more data. |
Model-agnostic Methods for Text Classification with Inherent Noise (2020.coling-industry)
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| Challenge: | Text classification is a fundamental problem in natural language processing, but its performance relies on high-quality annotations. |
| Approach: | They propose to use model-agnostic methods to handle inherent noise in large scale text classification that can be easily incorporated into existing machine learning workflows with minimal interruption. |
| Outcome: | The proposed method outperforms baselines by up to 10% in classification accuracy while requiring no network modifications. |
Identification of Multiword Expressions in Tweets for Hate Speech Detection (2022.lrec-1)
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| Challenge: | Multiword expression (MWE) identification in tweets is a complex task due to the complex linguistic nature of MWEs combined with the non-standard language use in social networks. |
| Approach: | They propose a new architecture for incorporating multiword expression features into tweets to improve their accuracy. |
| Outcome: | The proposed system outperforms existing systems on the hate speech detection task on English Twitter. |
NeuronBlocks: Building Your NLP DNN Models Like Playing Lego (D19-3)
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| Challenge: | Deep Neural Networks (DNN) have been widely employed in industry to address various natural language processing tasks. |
| Approach: | They propose an NLP toolkit that encapsulates neural network modules as building blocks to construct various DNN models with complex architecture. |
| Outcome: | The proposed toolkit can build, train, and test various DNN models with complex architecture. |
Supervised Gradual Machine Learning for Aspect-Term Sentiment Analysis (2023.tacl-1)
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| Challenge: | Recent work shows that Aspect-Term Sentiment Analysis (ATSA) can be performed by Gradual Machine Learning (GML) but the current unsupervised solution is limited by inaccurate knowledge conveyance. |
| Approach: | They propose a supervised approach which leverages binary polarity relations between instances to enable supervised knowledge conveyance. |
| Outcome: | The proposed approach outperforms pure DNN solutions on real benchmark data. |
DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis (2021.findings-acl)
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| Challenge: | Existing methods for Aspect-Term Sentiment Analysis (ATSA) use pre-specified lexicons to extract sentiment features. |
| Approach: | They propose a Deep Neural Network-driven approach for Aspect-Term Sentiment Analysis (ATSA) that leverages shared features between labeled and unlabeled instances for knowledge conveyance. |
| Outcome: | The proposed approach consistently achieves state-of-the-art performance on real benchmark data. |
Locally Distributed Activation Vectors for Guided Feature Attribution (2022.coling-1)
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| Challenge: | Existing methods to explain predictions of deep neural networks are unstable and do not always provide faithful explanations to the target model. |
| Approach: | They propose a method to learn explanations-specific representations while constructing deep network models for text classification. |
| Outcome: | The proposed method improves model interpretability while preserving predictive performance. |
FAtNet: Cost-Effective Approach Towards Mitigating the Linguistic Bias in Speaker Verification Systems (2022.findings-naacl)
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| Challenge: | Linguistic bias in Deep Neural Network (DNN) based systems is a critical challenge that needs attention. |
| Approach: | They propose to integrate a lightweight embedding with existing NLP systems to mitigate linguistic bias without adaptation. |
| Outcome: | The proposed framework reduces linguistic bias and enhances usability of baselines for twelve languages. |
Domain Adaptation with Adversarial Training and Graph Embeddings (P18-1)
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| Challenge: | Existing models for deep neural networks can handle data distributions between source and target domains, but they must deal with data distribution drifts. |
| Approach: | They propose a model that leverages unlabeled and labeled data from a related domain to deal with distribution drifts. |
| Outcome: | The proposed model improves over baselines on two real-world disaster datasets. |
A New Concept of Deep Reinforcement Learning based Augmented General Tagging System (C18-1)
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| Challenge: | Existing systems for general sequence tagging/labeling are based on neural network architectures. |
| Approach: | They propose a deep neural network based sequence labeling model and a augmented tagger to improve system performance by modeling the data with minority tags. |
| Outcome: | The proposed system outperforms the current state-of-the-art model on ATIS and CoNLL-2003 datasets by 1.9% and 1.4%. |
Transfer Learning for Text Classification via Model Risk Analysis (2024.findings-emnlp)
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| Challenge: | Existing approaches to text classification are limited by distribution drift and misprediction risk. |
| Approach: | They propose a model risk analysis approach to adapt a pre-trained DNN model to a new dataset given only a small set of representative data. |
| Outcome: | The proposed model performs considerably better than existing approaches on real datasets. |
Extracting Chemical-Protein Interactions via Calibrated Deep Neural Network and Self-training (2020.findings-emnlp)
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| Challenge: | Several natural language processing methods have been used to extract interactions between chemicals and proteins from biomedical text data. |
| Approach: | They propose a method to extract chemical–protein interactions from biomedical text data . they use a pre-trained language-understanding model and calibration techniques to estimate uncertainty . |
| Outcome: | The proposed approach achieves state-of-the-art performance on the Biocreative VI ChemProt task while preserving higher calibration abilities. |
Deep Dominance - How to Properly Compare Deep Neural Models (P19-1)
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| Challenge: | Existing methods for comparing DNNs on unseen data are not suitable for this task. |
| Approach: | They propose to adapt a test for the Almost Stochastic Dominance relation between two distributions to the problem by comparing their performance on unseen data. |
| Outcome: | The proposed method meets all criteria while previously proposed methods fail to do so. |
Semi-supervised Autoencoding Projective Dependency Parsing (2020.coling-main)
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| Challenge: | Existing models for semi-supervised dependency parsing use labeled data, but they require large amounts of labeles. |
| Approach: | They propose two end-to-end autoencoding models for semi-supervised graph-based projective dependency parsing. |
| Outcome: | The proposed models outperform a semi-supervised model on WSJ and UD dependency parsing data sets. |
End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories (P19-1)
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| Challenge: | Existing sequence tagging models do not explicitly exploit linguistic theories of metaphor identification. |
| Approach: | They propose to exploit linguistic theories of metaphor identification in deep neural networks to improve model performance. |
| Outcome: | The proposed models achieve state-of-the-art in end-to-end metaphor identification on three datasets. |
Towards Emotion-aided Multi-modal Dialogue Act Classification (2020.acl-main)
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| Challenge: | Considerable work on Dialogue Act Classification (DAC) has been done on textual inputs. |
| Approach: | They propose to use a multimodal Emotion aware Dialogue Act dataset to explore the role of multi-modality and emotion recognition in DAC. |
| Outcome: | The proposed dataset shows that multi-modality and emotion recognition improves DAC performance compared to uni-modal and single task DAC variants. |
Sampling Bias in Deep Active Classification: An Empirical Study (D19-1)
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| Challenge: | Existing studies on active learning identify sampling bias in large datasets . cost and time needed for labeling and model training are bottlenecks preventing new and/or better models from being trained . |
| Approach: | They propose to use active learning to identify representative data samples for training . they propose to create tiny datasets that can be used for cheap training if needed . |
| Outcome: | The proposed model outperforms the state-of-the-art on active text classification using small representative datasets with active learning. |
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds (D19-1)
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| Challenge: | Incorporating Item Response Theory (IRT) into NLP tasks can provide valuable information about model performance and behavior. |
| Approach: | They propose to use IRT models generated from artificial crowds of DNNs to learn IRT. |
| Outcome: | The proposed model learning method outperforms baseline methods for two NLP tasks. |
Learning with Noisy Labels for Sentence-level Sentiment Classification (D19-1)
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| Challenge: | Existing research on learning with noisy labels dates back to the 1980s, but it is still vibrant today. |
| Approach: | They propose a novel DNN model called NetAb to deal with noisy labels during training and train the networks using their respective loss functions in mutual reinforcement. |
| Outcome: | The proposed model can fit training data with noisy labels and predict clean labels. |
Progress in Multilingual Speech Recognition for Low Resource Languages Kurmanji Kurdish, Cree and Inuktut (2022.lrec-1)
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| Challenge: | Using acoustic data, we develop automatic speech recognition systems for three low resource languages. |
| Approach: | They develop automatic speech recognition systems for three low resource languages using acoustic training data from 12 different languages in the hybrid DNN/HMM framework. |
| Outcome: | The proposed models are for three low resource languages: Kurmanji Kurdish, Cree and Inuktut. |
Learning by Analogy: Diverse Questions Generation in Math Word Problem (2023.findings-acl)
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| Challenge: | Existing methods for solving math word problem (MWP) use shortcut learning to train solvers based on samples with a single question. |
| Approach: | They propose to generate diverse yet consistent questions from a common scenario . they then feed the equations to a question generator to obtain the diverse questions . their method leads to performance improvement on the current benchmark Math23K . |
| Outcome: | The proposed method generates diverse yet consistent questions with a variety of equations and questions . it improves on the current benchmark, which is based on the proposed method . |
DNN-based Speech Synthesis Using Abundant Tags of Spontaneous Speech Corpus (2020.lrec-1)
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Yuki Yamashita, Tomoki Koriyama, Yuki Saito, Shinnosuke Takamichi, Yusuke Ijima, Ryo Masumura, Hiroshi Saruwatari
| Challenge: | Experimental evaluation results show that rich annotations enhance the reproducibility of paralinguistic features of synthetic speech. |
| Approach: | They investigate the effectiveness of using rich annotations in deep neural network-based statistical speech synthesis. |
| Outcome: | The proposed method improves reproducibility of paralinguistic features of synthetic speech . the corpus of spontaneous Japanese (CSJ) has large annotations on paralinguistic and nonlinguistic features . |
Lexical Tone Recognition in Mizo using Acoustic-Prosodic Features (2020.lrec-1)
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| Challenge: | Mizo is an under-studied Tibeto-Burman tonal language of the Northeast of India. |
| Approach: | They propose to use acoustic-prosodic parameters to automatically recognize four phonological tones in Mizo using a set of features computed from Fundamental Frequency contours. |
| Outcome: | The proposed model performs better than the existing classifiers in recognizing four phonological tones in Mizo using acoustic-prosodic parameters. |
Selective Knowledge Distillation: Fusing LLM Semantic Strengths with DNN Efficiency for Binary Code Similarity Detection (2026.acl-long)
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| Challenge: | BinSKD is a binary code similarity detection technique that can be used in bug detection, patch analysis, and malware detection. |
| Approach: | They propose to leverage an LLM-based BCSD method as the teacher model and transfer its knowledge of high-level program semantics to various DNN-based student models. |
| Outcome: | The proposed method yields Recall@1 improvements of 14.5%–91.2% for DNN-based BCSD methods and enables HermesSim to match the teacher’s performance with orders-of-magnitude efficiency. |