Papers with deep learning framework
SMARTR: A Framework for Early Detection using Survival Analysis of Longitudinal Texts (2024.naacl-srw)
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| Challenge: | a paper aims to detect expensive insurance claims early using textual information from claims notes. |
| Approach: | They propose a model that leverages survival analysis concepts from claims notes to enhance a posteriori classification and early detection. |
| Outcome: | The proposed model improves classification and early detection without reducing performance . it is based on a privately held corpus of claim files from a Canadian insurer . |
Point-of-Interest Oriented Question Answering with Joint Inference of Semantic Matching and Distance Correlation (2020.aacl-main)
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| Challenge: | Existing methods for POI oriented question answering lack ability to handle important POI related information. |
| Approach: | They propose a deep learning framework integrated with joint inference to capture tag semantic and geographic correlation between question and POIs. |
| Outcome: | The proposed model captures both tag semantic and geographic correlation between question and POIs. |
XferBench: a Data-Driven Benchmark for Emergent Language (2024.naacl-long)
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| Challenge: | Existing methods to teach models to "language" are full of bias, toxicity, and potential intellectual property violations. |
| Approach: | They propose a benchmark for evaluating the overall quality of emergent languages using data-driven methods. |
| Outcome: | The proposed benchmark is based on utterances from the emergent language and is validated using human, synthetic, and emergentic language baselines. |
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering (2020.aacl-main)
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| Challenge: | Existing techniques for visual question answering focus on English questions, but many applications require a multilingual module. |
| Approach: | They propose a deep learning framework for multilingual and code- mixed visual question answering . they create Hindi and Code-mixed VQA datasets by exploiting linguistic properties of these languages . |
| Outcome: | The proposed model is capable of predicting answers from the questions in Hindi, English or Code- mixed (Hindi-English) languages. |
Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network (N19-1)
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| Challenge: | In order to ensure customer satisfaction and retention, it is imperative for customer care agents and chatbots to be cordial and emphatic to the customer. |
| Approach: | They propose a deep learning framework that automatically transforms neutral customer care responses into courteous replies by stylistic transfer. |
| Outcome: | The proposed model can generate courteous expressions consistent with the emotional state of the customer while preserving the content. |
Syntax for Semantic Role Labeling, To Be, Or Not To Be (P18-1)
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| Challenge: | Existing neural SRL models lack syntactic backbone for performance, limiting its use in deep learning. |
| Approach: | They propose an enhanced argument labeling model with extended korder argument pruning algorithm for effectively exploiting syntactic information. |
| Outcome: | The proposed model achieves state-of-the-art on the CoNLL-2008 and 2009 benchmarks in English and Chinese. |
Stock Embeddings Acquired from News Articles and Price History, and an Application to Portfolio Optimization (2020.acl-main)
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| Challenge: | Recent studies have shown that news articles can be leveraged to improve price prediction. |
| Approach: | They propose a method to encode the influence of news articles through a vector representation of stocks . they use a deep learning framework to acquire the vector representation using news articles and price history . |
| Outcome: | The proposed method can be applied to other financial problems besides price prediction. |
DeepTC – An Extension of DKPro Text Classification for Fostering Reproducibility of Deep Learning Experiments (L18-1)
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| Challenge: | a current state of DKPro TC does not allow integration of deep learning . we integrate Keras, DyNet, and DeepLearning4J as proof-of-concept . |
| Approach: | They propose a deep learning extension for the multi-purpose text classification framework DKPro Text Classification. |
| Outcome: | The proposed extension improves readability and reduces redundant source code. |
Incorporating Politeness across Languages in Customer Care Responses: Towards building a Multi-lingual Empathetic Dialogue Agent (2020.lrec-1)
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| Challenge: | Qualitative and quantitative analysis shows that our proposed model can converse in both the languages and the information shared between the languages helps in improving the performance of the overall system. |
| Approach: | They propose a deep learning framework that can handle different languages and incorporate courteous behaviour in generic customer care responses in a multi-lingual scenario. |
| Outcome: | The proposed model can converse in both languages and the information shared between the languages helps in improving the overall performance of the system. |
A deep-learning framework to detect sarcasm targets (D19-1)
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| Challenge: | Existing methods for sarcasm target detection are difficult to implement in natural language processing. |
| Approach: | They propose a deep learning framework for sarcasm target detection in predefined sarkastic texts. |
| Outcome: | The proposed framework improves accuracy and accuracy in match and dice scores compared to the current state-of-the-art framework. |