Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding
@inproceedings{Louvan2018ExploringNE, title={Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding}, author={Samuel Louvan and Bernardo Magnini}, booktitle={SCAI@EMNLP}, year={2018} }
Slot filling is a crucial task in the Natural Language Understanding (NLU) component of a dialogue system. Most approaches for this task rely solely on the domain-specific datasets for training. We propose a joint model of slot filling and Named Entity Recognition (NER) in a multi-task learning (MTL) setup. Our experiments on three slot filling datasets show that using NER as an auxiliary task improves slot filling performance and achieve competitive performance compared with state-of-the-art…
10 Citations
Auxiliary Capsules for Natural Language Understanding
- Computer ScienceICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- 2020
This work extends the newly introduced application of Capsule Networks for NLU to a multi-task learning environment, using relevant auxiliary tasks, and performs joint Intent classification and Slot filling with the aid of Named Entity Recognition (NER) and Part of Speech (POS) tagging tasks.
AISFG: Abundant Information Slot Filling Generator
- Computer ScienceNAACL
- 2022
This work proposes Abundant Information Slot Filling Generator (AISFG), a generative model with a novel query template that incorporates domain descriptions, slot descriptions, and examples with context that outperforms state-of-the-art approaches in zero/few-shot slot filling task.
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- Computer ScienceSUSTAINLP
- 2020
This paper proposes a novel two-stage model architecture that can be trained with only a few in-domain hand-labeled examples that outperforms other state-of-art systems on the SNIPS benchmark dataset.
On the Use of External Data for Spoken Named Entity Recognition
- Computer ScienceNAACL
- 2022
This work considers self-training, knowledge distillation, and transfer learning for end-to-end (E2E) and pipeline (speech recognition followed by text NER) approaches and finds that several of these approaches improve performance in resource-constrained settings beyond the benefits from pre-trained representations.
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- Computer ScienceACM Transactions on Asian and Low-Resource Language Information Processing
- 2022
This paper presents a framework which exploits the semantic clusters among the question-answer pairs to compensate for the lack of enough training data, and outperforms the standard sequence to sequence model by a large margin in terms of ROUGE and BLEU scores.
FASTDial: Abstracting Dialogue Policies for Fast Development of Task Oriented Agents
- Computer ScienceACL
- 2019
A novel abstraction framework called FASTDial for designing task oriented dialogue agents, built on top of the OpenDial toolkit, that allows for minimizing programming effort and domain expert training time, by hiding away many implementation details.
Polarity enriched attention network for aspect-based sentiment analysis
- Computer ScienceInternational Journal of Information Technology
- 2022
Methods to enhance sentiment granularity at the aspect-level by using the proposed PEAN model, which substantially improves the performance of ABSA on employed datasets and is illustrated with two benchmark sentiment datasets.
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging
- Computer ScienceEMNLP
- 2020
This work investigates how to effectively use unlabeled data by exploring the task-specific semi-supervised approach, Cross-View Training (CVT) and comparing it with task-agnostic BERT in multiple settings that include domain and task relevant English data.
Neural MOS Prediction for Synthesized Speech Using Multi-Task Learning with Spoofing Detection and Spoofing Type Classification
- Computer Science2021 IEEE Spoken Language Technology Workshop (SLT)
- 2021
A multi-task learning (MTL) method to improve the performance of a MOS prediction model using the following two auxiliary tasks: spoofing detection (SD) and spoofing type classification (STC).
A Survey of Joint Intent Detection and Slot Filling Models in Natural Language Understanding
- Computer ScienceACM Comput. Surv.
- 2023
This survey brings the coverage of methods up to 2021 including the many applications of deep learning in the field and looks at issues addressed in the joint task and the approaches designed to address these issues.
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