Papers with Deep Learning
A Tutorial on Evaluation Metrics used in Natural Language Generation (2021.naacl-tutorials)
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| Challenge: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |
| Approach: | This tutorial presents the evolution of automatic evaluation metrics to their current state . it aims to assess the extent of scientific progress made and identify areas/components that need improvement . |
| Outcome: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |
Unsupervised Question Answering for Fact-Checking (D19-66)
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| Challenge: | Recent Deep Learning (DL) models have achieved human-level accuracy on natural language tasks such as question-answering, natural language inference, and textual entailment. |
| Approach: | They propose an unsupervised question-answering based approach for a similar task, fact-checking. |
| Outcome: | The proposed approach achieves label accuracy of 80.2% on the development set and 80.25% on the test set. |
Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks (N19-3)
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| Challenge: | Automated extraction methods for vowels are available, but coding rhoticity has lagged behind. |
| Approach: | They use Neural Networks/Deep Learning to train a model on 208 speakers in Boston . they find that there is no reliable method for classifying r-dropping . |
| Outcome: | The proposed method trains a model on 208 speakers in Boston, Massachusetts. |
Do Deep Neural Nets Display Human-like Attention in Short Answer Scoring? (2022.naacl-main)
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| Challenge: | DL-based graders often lack the ability to explain and justify how a prediction is made, which decreases their trustworthiness and hinders educators from embracing them in practice. |
| Approach: | They conducted a user study to determine whether DL-based graders align with human grader . they also ran a randomized controlled experiment to explore the impact of highlighting important words detected by DL grader. |
| Outcome: | The proposed method enables human graders to identify important words when marking short answer questions. |
Agent Assist through Conversation Analysis (2020.emnlp-demos)
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Kshitij Fadnis, Nathaniel Mills, Jatin Ganhotra, Haggai Roitman, Gaurav Pandey, Doron Cohen, Yosi Mass, Shai Erera, Chulaka Gunasekara, Danish Contractor, Siva Patel, Q. Vera Liao, Sachindra Joshi, Luis Lastras, David Konopnicki
| Challenge: | Using conversational approach to information retrieval for agent assistance, customer support agents are a critical part of an organization's customer support team. |
| Approach: | They propose a conversational approach to information retrieval for agent assistance that monitors an evolving conversation and recommends both responses and URLs of documents. |
| Outcome: | The proposed system monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. |
Gold Standard Bangla OCR Dataset: An In-Depth Look at Data Preprocessing and Annotation Processes (2023.emnlp-industry)
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| Challenge: | Existing datasets designed specifically for the Bengali language have been limited. |
| Approach: | They propose to use a large collection of labeled Bangla text image datasets to improve the performance of Bangla OCR. |
| Outcome: | The proposed system is the most extensive gold standard corpus for Bangla characters and words, comprising over 4 million human-annotated images. |
CUPID: Curriculum Learning Based Real-Time Prediction using Distillation (2023.acl-industry)
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| Challenge: | Relevance in E-commerce Product Search is crucial for providing customers with accurate results that match their query intent. |
| Approach: | They propose a curriculum learning based real-time relevance prediction using distillation . they propose e-commerce search systems that use transformers to predict relevance . |
| Outcome: | The proposed model improves on english and Arabic in a bi-lingual relevance prediction task while maintaining low evaluation latency on CPUs. |
Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance (2021.naacl-main)
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| Challenge: | Recent work on entity coreference resolution (CR) follows current trends in Deep Learning . traditional approaches do not make use of hierarchical representations of discourse structure . |
| Approach: | They propose to leverage automatically constructed discourse parse trees within a neural approach to generate anaphoric mentions. |
| Outcome: | The proposed model improves on two benchmark entity coreference-resolution datasets. |
Exploring hybrid approaches to readability: experiments on the complementarity between linguistic features and transformers (2024.findings-eacl)
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| Challenge: | Linguistic features have been a key component of the automatic assessment of text readability (ARA) with the development in the ARA field, the research moved to Deep Learning (DL) |
| Approach: | They compare 6 hybrid approaches to Machine Learning and DL on 4 corpora and found they are the most robust on smaller datasets and across languages. |
| Outcome: | The proposed approaches perform better on smaller datasets and across languages and tasks. |
Experimental Standards for Deep Learning in Natural Language Processing Research (2022.findings-emnlp)
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Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein, Daniel Varab, Mike Zhang, Rob van der Goot, Christian Hardmeier, Barbara Plank
| Challenge: | a lack of common experimental standards remains an open challenge to the field at large . |
| Approach: | They propose to distill discussions on experimental standards into a single, widely-applicable methodology. |
| Outcome: | Using best practices, we can strengthen experimental evidence, improve reproducibility and enable scientific progress. |
RAG-KT: Cross-platform Explainable Knowledge Tracing with Multi-view Fusion Retrieval Generation (2026.findings-acl)
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| Challenge: | Conventional Deep Learning (DL)-based KT models are tied to platform-specific identifiers and latent representations, making them hard to transfer and interpret. |
| Approach: | They propose a retrieval-augmented paradigm that frames cross-platform KT as reliable context constrained inference with LLMs. |
| Outcome: | Experiments on three public KT benchmarks show that the proposed paradigm improves accuracy and robustness, and also shows strong performance under cross-platform conditions. |
Subsequence Based Deep Active Learning for Named Entity Recognition (2021.acl-long)
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| Challenge: | Active Learning (AL) has been successfully applied to Deep Learning to drastically reduce the amount of data required to achieve high performance. |
| Approach: | They propose to query subsequences within sentences and propagate their labels to other sentences. |
| Outcome: | The proposed approach achieves high performance on OntoNotes 5.0 and CoNLL 2003 with only 13% of training data and 27% of the training data. |
Development of Automatic Speech Recognition for the Documentation of Cook Islands Māori (2022.lrec-1)
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Rolando Coto-Solano, Sally Akevai Nicholas, Samiha Datta, Victoria Quint, Piripi Wills, Emma Ngakuravaru Powell, Liam Koka’ua, Syed Tanveer, Isaac Feldman
| Challenge: | a new study describes the process of data processing and training of an automatic speech recognition system for Cook Islands Mori . the system is based on statistical and Deep Learning techniques, and is available under a license . |
| Approach: | They describe the process of data processing and training of an automatic speech recognition system for Cook Islands Mori . they transcribed four hours of speech from adults and elderly speakers of the language and prepared two experiments . |
| Outcome: | The proposed system can perform better with low-resource Indigenous languages . the system can be used to accelerate the documentation of Cook Islands Mori . |
Evaluation of Automatic Formant Trackers (L18-1)
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| Challenge: | Formant trackers are widely used by speech scientists and speech engineers. |
| Approach: | They propose to use four open source formant trackers to evaluate the quality of speech recognition algorithms on the same American English data set. |
| Outcome: | The proposed formant trackers outperform LPC-based and Deep Learning on the American English data set VTR-TIMIT. |
ReinforceBug: A Framework to Generate Adversarial Textual Examples (2021.naacl-main)
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| Challenge: | Recent studies have demonstrated that ML Models are sensitive to Adversarial Examples (AEs) AEs are generated by perturbingining examples that preserve the intrinsic utility of the ML solutions but influence the classifier's predictions between original and modified inputs. |
| Approach: | They propose a reinforcement learning framework that learns a policy that is transferable on unseen datasets and generates utility-preserving and transferable AEs. |
| Outcome: | The proposed framework is 10% more successful than the state-of-the-art attack TextFooler. |