# GKS: Graph-based Knowledge Selector for Task-oriented Dialog System

arXiv: https://arxiv.org/abs/2112.03719 · HTML: https://mlsanity.com/paper/2112.03719/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://mlsanity.com/paper/1901.08149/) (2019)
- [PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable](https://mlsanity.com/paper/1910.07931/) (2019)
- [Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1907.05774/) (2019)
- [Fine-grained Fact Verification with Kernel Graph Attention Network](https://mlsanity.com/paper/1910.09796/) (2019)
- [GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification](https://mlsanity.com/paper/1908.01843/) (2019)
- [Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access](https://mlsanity.com/paper/2006.03533/) (2020)
- [Robust Zero-Shot Cross-Domain Slot Filling with Example Values](https://mlsanity.com/paper/1906.06870/) (2019)
- [Schema-Guided Dialogue State Tracking Task at DSTC8](https://mlsanity.com/paper/2002.01359/) (2020)
- [Learning to Select External Knowledge with Multi-Scale Negative Sampling](https://mlsanity.com/paper/2102.02096/) (2021)
- [Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access Track in DSTC9](https://mlsanity.com/paper/2101.09276/) (2021)
- [Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2012.11937/) (2020)

## Built on by (most cited first, 1 of 1)

- [TOD-Flow: Modeling the Structure of Task-Oriented Dialogues](https://mlsanity.com/paper/2312.04668/) (2023)

## Abstract (arXiv)

In previous research, knowledge-selection tasks mostly rely on language model-based methods or knowledge ranking. However, while approaches that rely on the language models take all knowledge as sequential input, knowledge does not contain sequential information in most circumstances. On the other hand, the knowledge-ranking methods leverage dialog history and each given knowledge snippet separately, but they do not consider information between knowledge snippets. In the Tenth Dialog System Technology Challenges (DSTC10), we participated in the second Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations. To deal with the problems mentioned above, we modified training methods based on state-of-the-art (SOTA) models for the first and third sub-tasks. As for the second sub-task of knowledge selection, we proposed Graph-Knowledge Selector (GKS), utilizing a graph-attention base model incorporated with the language model. GKS makes knowledge-selection decisions in the dialog by simultaneously considering each knowledge embedding generated from the language model without sequential features. Moreover, GKS leverages considerable knowledge in decision-making and takes relations across knowledge as part of the selection process. As a result, GKS outperforms several SOTA models proposed in the data-set on knowledge selection from the Ninth Dialog System Technology Challenges (DSTC9).