Papers with model designs
A Closer Look at Data Bias in Neural Extractive Summarization Models (D19-54)
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| Challenge: | In this paper, we examine the generalization behaviour of summarization models . we propose several properties of datasets that matter for generalization . |
| Approach: | They propose several properties of datasets which matter for generalization of summarization models. |
| Outcome: | The proposed approach improves the state-of-the-art model by rethinking the model design process on a typical dataset. |
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework (D19-1)
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| Challenge: | generative models for end-to-end sequence generation have been shown promising for this task . however, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator is still challenging. |
| Approach: | They propose a framework where skeleton extraction is made by an interpretable matching model and a retrieval-guided response generator is followed by a separate generator. |
| Outcome: | The proposed framework outperforms baseline models in a variety of experiments. |
EDC: Effective and Efficient Dialog Comprehension For Dialog State Tracking (2024.naacl-long)
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| Challenge: | Existing methods for dialog state tracking face trade-offs between accuracy and efficiency . effective and efficient dialog comprehension (EDC) predicts domains, slot names and slot values of dialog state step-by-step for better accuracy . |
| Approach: | They propose an alternative method that leverages the tree structure of the dialog state. |
| Outcome: | The proposed approach achieves state-of-the-art JGA accuracy and is more efficient than previous models. |
An Empirical Study on Neural Keyphrase Generation (2021.naacl-main)
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| Challenge: | Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. |
| Approach: | They propose to compare the generalizability of KPG models with other models by analyzing the most crucial factors that may affect their generalizarability. |
| Outcome: | The proposed model can be used to predict keyphrases from a set of input sequences, and it can be compared with existing models. |
A Simple Yet Strong Pipeline for HotpotQA (2020.emnlp-main)
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| Challenge: | Existing models for multi-hop question answering have been proposed with varying complexities. |
| Approach: | They propose to use BERT to identify potentially relevant sentences independently of each other . they feed selected sentences into a standard BERT span prediction model to choose an answer . |
| Outcome: | The proposed pipeline outperforms existing models on hotpotQA and support identification. |
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning (2023.emnlp-main)
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| Challenge: | Existing passive mental health monitoring systems use alternative features such as activity, app usage, and location via smartphones due to data privacy concerns. |
| Approach: | They propose a mobile mental health monitoring system that utilizes continuous speech and keyboard input in a privacy-preserving way via federated learning. |
| Outcome: | The proposed system achieves 0.15 AUROC improvement and 8.21% MAE reduction in self-reported depression, stress, anxiety, and mood from 46 participants. |