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Found 36 papers, 17 papers with code
Uncertainty and Traffic-Aware Active Learning for Semantic Parsing
Collecting training data for semantic parsing is a time-consuming and expensive task.
Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation
While state-of-the-art Dialogue State Tracking (DST) models show promising results, all of them rely on a traditional cross-entropy loss function during the training process, which may not be optimal for improving the joint goal accuracy.
Integrated Weak Learning
We introduce a label model that can learn to aggregate weak supervision sources differently for different datapoints and takes into consideration the performance of the end-model during training.
ViralBERT: A User Focused BERT-Based Approach to Virality Prediction
We collect a dataset of 330k tweets to train ViralBERT and validate the efficacy of our model using baselines from current studies in this field.
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking
Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
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ASSIST: Towards Label Noise-Robust Dialogue State Tracking
In this paper, instead of improving the annotation quality further, we propose a general framework, named ASSIST (lAbel noiSe-robuSt dIalogue State Tracking), to train DST models robustly from noisy labels.
Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?
The user questionnaire revealed that participants found the Content Flow Bar helpful and enjoyable for finding relevant information in videos.
Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems
In informational recommenders, many challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas.
Variational Continual Bayesian Meta-Learning
Conventional meta-learning considers a set of tasks from a stationary distribution.
Towards More Accountable Search Engines: Online Evaluation of Representation Bias
Information availability affects people's behavior and perception of the world.
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations
Predominantly, two formulations are used for sentence-pair tasks: bi-encoders and cross-encoders.
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Sample Efficient Model Evaluation
Labelling data is a major practical bottleneck in training and testing classifiers.
PEEK: A Large Dataset of Learner Engagement with Educational Videos
One of the main challenges in advancing this research direction is the scarcity of large, publicly available datasets.
Estimation of Fair Ranking Metrics with Incomplete Judgments
There is increasing attention to evaluating the fairness of search system ranking decisions.
MS MARCO: Benchmarking Ranking Models in the Large-Data Regime
Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard such as MS MARCO, are intended to encourage research and track our progress, addressing big questions in our field.
TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime
The TREC Deep Learning (DL) Track studies ad hoc search in the large data regime, meaning that a large set of human-labeled training data is available.
MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation
This work introduces MultiWOZ 2. 4, in which we refine all annotations in the validation set and test set on top of MultiWOZ 2. 1.
Significant Improvements over the State of the Art? A Case Study of the MS MARCO Document Ranking Leaderboard
Leaderboards are a ubiquitous part of modern research in applied machine learning.
Overview of the TREC 2020 deep learning track
This is the second year of the TREC Deep Learning Track, with the goal of studying ad hoc ranking in the large training data regime.
Slot Self-Attentive Dialogue State Tracking
Then a stacked slot self-attention is applied on these features to learn the correlations among slots.
VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement
This paper introduces VLEngagement, a novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures and several metrics related to user engagement.
Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling
Semantic hashing is a powerful paradigm for representing texts as compact binary hash codes.
ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search
Users of Web search engines reveal their information needs through queries and clicks, making click logs a useful asset for information retrieval.
Predicting Engagement in Video Lectures
The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners.
On the Reliability of Test Collections for Evaluating Systems of Different Types
As deep learning based models are increasingly being used for information retrieval (IR), a major challenge is to ensure the availability of test collections for measuring their quality.
Overview of the TREC 2019 deep learning track
The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime.
Towards an Integrative Educational Recommender for Lifelong Learners
One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning.
TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources
The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality education to large masses of learners.
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs
While a wide range of interpretable generative procedures for graphs exist, matching observed graph topologies with such procedures and choices for its parameters remains an open problem.
Self-Attentive Hawkes Processes
The proposed method adapts self-attention to fit the intensity function of Hawkes processes.
Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks
Deep neural IR models, in contrast, compare the whole query to the document and are, therefore, typically employed only for late stage re-ranking.
Variational Self-attention Model for Sentence Representation
This paper proposes a variational self-attention model (VSAM) that employs variational inference to derive self-attention.
Towards Task Understanding in Visual Settings
We consider the problem of understanding real world tasks depicted in visual images.
Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach
As a result, significant amount of research has been devoted to extracting proper representations of tasks in order to enable search systems to help users complete their tasks, as well as providing the end user with better query suggestions, for better recommendations, for satisfaction prediction, and for improved personalization in terms of tasks.
