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… 

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References

SHOWING 1-10 OF 26 REFERENCES

Bag of Experts Architectures for Model Reuse in Conversational Language Understanding

This work describes Bag of Experts (BoE) architectures for model reuse for both LSTM and CRF based models for slot tagging and shows that these models outperform the baseline models with a statistically significant average margin of 5.06% in absolute F1-score.

Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM

Experimental results show the power of a holistic multi-domain, multi-task modeling approach to estimate complete semantic frames for all user utterances addressed to a conversational system over alternative methods based on single domain/task deep learning.

A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling

New Bi-model based RNN semantic frame parsing network structures are designed to perform the intent detection and slot filling tasks jointly, by considering their cross-impact to each other using two correlated bidirectional LSTMs (BLSTM).

Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding

The proposed multi-task model delivers better performance with less data by leveraging patterns that it learns from the other tasks, and supports an open vocabulary, which allows the models to generalize to unseen words.

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

A slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization is proposed.

New Transfer Learning Techniques for Disparate Label Sets

This work proposes a solution based on label embeddings induced from canonical correlation analysis (CCA) that reduces the problem to a standard domain adaptation task and allows use of a number of transfer learning techniques.

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

A novel neutral network architecture is introduced that benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF, thus making it applicable to a wide range of sequence labeling tasks.

What is left to be understood in ATIS?

It is concluded that even with such low error rates, ATIS test set still includes many unseen example categories and sequences, hence requires more data, and new annotated larger data sets from more complex tasks with realistic utterances can avoid over-tuning in terms of modeling and feature design.

Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

This work proposes an attention-based neural network model for joint intent detection and slot filling, both of which are critical steps for many speech understanding and dialog systems.

Multi-Domain Adversarial Learning for Slot Filling in Spoken Language Understanding

It is shown that adversarial training helps in learning better domain-general SLU models, leading to improved slot filling F1 scores, and applying adversarial learning on domain- general model also helps in achieving higher slot filling performance when the model is jointly optimized with domain-specific models.