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Found 22 papers, 12 papers with code
GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution
Learning from multimodal data has become a popular research topic in recent years.
Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning
Dialogue state tracking (DST) is an important step in dialogue management to keep track of users' beliefs.
Context-Situated Pun Generation
In this work, we propose a new task, context-situated pun generation, where a specific context represented by a set of keywords is provided, and the task is to first identify suitable pun words that are appropriate for the context, then generate puns based on the context keywords and the identified pun words.
Towards Textual Out-of-Domain Detection without In-Domain Labels
In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong actions.
Building Goal-Oriented Dialogue Systems with Situated Visual Context
Most popular goal-oriented dialogue agents are capable of understanding the conversational context.
Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems
Most prior work on task-oriented dialogue systems is restricted to supporting domain APIs.
Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE
InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years.
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems
Finally, we evaluate our system using a typical movie ticket booking task and show that the dialogue simulator is an essential component of the system that leads to over $50\%$ improvement in turn-level action signature prediction accuracy.
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap
In this paper, we propose using machine reading comprehension (RC) in state tracking from two perspectives: model architectures and datasets.
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents
Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents.
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
Machine Reading Comprehension (MRC) for question answering (QA), which aims to answer a question given the relevant context passages, is an important way to test the ability of intelligence systems to understand human language.
Dialog State Tracking: A Neural Reading Comprehension Approach
In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation.
Ranked #19 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
MultiWOZ 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines
To fix the noisy state annotations, we use crowdsourced workers to re-annotate state and utterances based on the original utterances in the dataset.
Ranked #16 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Invariant Representations without Adversarial Training
Representations of data that are invariant to changes in specified factors are useful for a wide range of problems: removing potential biases in prediction problems, controlling the effects of covariates, and disentangling meaningful factors of variation.
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach
We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses.
Auto-Encoding Total Correlation Explanation
Advances in unsupervised learning enable reconstruction and generation of samples from complex distributions, but this success is marred by the inscrutability of the representations learned.
Kernelized Hashcode Representations for Relation Extraction
Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods.
Variational Information Maximization for Feature Selection
We demonstrate that approximations made by existing methods are based on unrealistic assumptions.
Sifting Common Information from Many Variables
Measuring the relationship between any pair of variables is a rich and active area of research that is central to scientific practice.
The DARPA Twitter Bot Challenge
A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes.
Understanding confounding effects in linguistic coordination: an information-theoretic approach
We revisit some of the previous studies that reported strong signatures of stylistic accommodation, and find that a significant part of the observed coordination can be attributed to a simple confounding effect - length coordination.
Efficient Estimation of Mutual Information for Strongly Dependent Variables
We demonstrate that a popular class of nonparametric mutual information (MI) estimators based on k-nearest-neighbor graphs requires number of samples that scales exponentially with the true MI.
