Effects of Naturalistic Variation in Goal-Oriented Dialog
- URL: http://arxiv.org/abs/2010.02260v1
- Date: Mon, 5 Oct 2020 18:13:45 GMT
- Title: Effects of Naturalistic Variation in Goal-Oriented Dialog
- Authors: Jatin Ganhotra, Robert Moore, Sachindra Joshi and Kahini Wadhawan
- Abstract summary: We investigate the impact of naturalistic variation on two goal-oriented datasets: bAbI dialog task and Stanford Multi-Domain dataset.
We propose new and more effective testbeds for both datasets, by introducing naturalistic variation by the user.
- Score: 12.49850843793842
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Existing benchmarks used to evaluate the performance of end-to-end neural
dialog systems lack a key component: natural variation present in human
conversations. Most datasets are constructed through crowdsourcing, where the
crowd workers follow a fixed template of instructions while enacting the role
of a user/agent. This results in straight-forward, somewhat routine, and mostly
trouble-free conversations, as crowd workers do not think to represent the full
range of actions that occur naturally with real users. In this work, we
investigate the impact of naturalistic variation on two goal-oriented datasets:
bAbI dialog task and Stanford Multi-Domain Dataset (SMD). We also propose new
and more effective testbeds for both datasets, by introducing naturalistic
variation by the user. We observe that there is a significant drop in
performance (more than 60% in Ent. F1 on SMD and 85% in per-dialog accuracy on
bAbI task) of recent state-of-the-art end-to-end neural methods such as BossNet
and GLMP on both datasets.
Related papers
- LLM-assisted Explicit and Implicit Multi-interest Learning Framework for Sequential Recommendation [50.98046887582194]
We propose an explicit and implicit multi-interest learning framework to model user interests on two levels: behavior and semantics.
The proposed EIMF framework effectively and efficiently combines small models with LLM to improve the accuracy of multi-interest modeling.
arXiv Detail & Related papers (2024-11-14T13:00:23Z) - ASPEST: Bridging the Gap Between Active Learning and Selective
Prediction [56.001808843574395]
Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain.
Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples.
In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
arXiv Detail & Related papers (2023-04-07T23:51:07Z) - MUG: Interactive Multimodal Grounding on User Interfaces [12.035123646959669]
We present MUG, a novel interactive task for multimodal grounding where a user and an agent work collaboratively on an interface screen.
Prior works modeled multimodal UI grounding in one round: the user gives a command and the agent responds to the command. MUG allows multiple rounds of interactions such that upon seeing the agent responses, the user can give further commands for the agent to refine or even correct its actions.
arXiv Detail & Related papers (2022-09-29T21:08:18Z) - Self-augmented Data Selection for Few-shot Dialogue Generation [18.794770678708637]
We adopt the self-training framework to deal with the few-shot MR-to-Text generation problem.
We propose a novel data selection strategy to select the data that our generation model is most uncertain about.
arXiv Detail & Related papers (2022-05-19T16:25:50Z) - Dynamic Relation Discovery and Utilization in Multi-Entity Time Series
Forecasting [92.32415130188046]
In many real-world scenarios, there could exist crucial yet implicit relation between entities.
We propose an attentional multi-graph neural network with automatic graph learning (A2GNN) in this work.
arXiv Detail & Related papers (2022-02-18T11:37:04Z) - Dual Task Framework for Debiasing Persona-grounded Dialogue Dataset [17.403065663306567]
We introduce a data-centric approach for the task of improving persona-conditioned dialogue agents.
Specifically, we augment relevant personas to improve dialogue dataset/agent, by leveraging the primal-dual structure of the two tasks.
Experiments on Persona-Chat show that our approach outperforms pre-trained LMs by an 11.7 point gain in terms of accuracy.
arXiv Detail & Related papers (2022-02-11T04:08:46Z) - Adversarial Deep Feature Extraction Network for User Independent Human
Activity Recognition [4.988898367111902]
We present an adversarial subject-independent feature extraction method with the maximum mean discrepancy (MMD) regularization for human activity recognition.
We evaluate the method on well-known public data sets showing that it significantly improves user-independent performance and reduces variance in results.
arXiv Detail & Related papers (2021-10-23T07:50:32Z) - Partner Matters! An Empirical Study on Fusing Personas for Personalized
Response Selection in Retrieval-Based Chatbots [51.091235903442715]
This paper makes an attempt to explore the impact of utilizing personas that describe either self or partner speakers on the task of response selection.
Four persona fusion strategies are designed, which assume personas interact with contexts or responses in different ways.
Empirical studies on the Persona-Chat dataset show that the partner personas can improve the accuracy of response selection.
arXiv Detail & Related papers (2021-05-19T10:32:30Z) - Few-Shot Visual Grounding for Natural Human-Robot Interaction [0.0]
We propose a software architecture that segments a target object from a crowded scene, indicated verbally by a human user.
At the core of our system, we employ a multi-modal deep neural network for visual grounding.
We evaluate the performance of the proposed model on real RGB-D data collected from public scene datasets.
arXiv Detail & Related papers (2021-03-17T15:24:02Z) - A Simple Language Model for Task-Oriented Dialogue [61.84084939472287]
SimpleTOD is a simple approach to task-oriented dialogue that uses a single, causal language model trained on all sub-tasks recast as a single sequence prediction problem.
This allows SimpleTOD to fully leverage transfer learning from pre-trained, open domain, causal language models such as GPT-2.
arXiv Detail & Related papers (2020-05-02T11:09:27Z) - Improving Multi-Turn Response Selection Models with Complementary
Last-Utterance Selection by Instance Weighting [84.9716460244444]
We consider utilizing the underlying correlation in the data resource itself to derive different kinds of supervision signals.
We conduct extensive experiments in two public datasets and obtain significant improvement in both datasets.
arXiv Detail & Related papers (2020-02-18T06:29:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.