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Found 21 papers, 2 papers with code
Eidos, INDRA, \& Delphi: From Free Text to Executable Causal Models
Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process.
MEEP: An Open-Source Platform for Human-Human Dialog Collection and End-to-End Agent Training
We create a new task-oriented dialog platform (MEEP) where agents are given considerable freedom in terms of utterances and API calls, but are constrained to work within a push-button environment.
Lightly-supervised Representation Learning with Global Interpretability
We propose a lightly-supervised approach for information extraction, in particular named entity classification, which combines the benefits of traditional bootstrapping, i. e., use of limited annotations and interpretability of extraction patterns, with the robust learning approaches proposed in representation learning.
Learning Discriminative Relational Features for Sequence Labeling
For learning features derived from inputs at a particular sequence position, we propose a Hierarchical Kernels-based approach (referred to as Hierarchical Kernel Learning for Structured Output Spaces - StructHKL).
Visual Supervision in Bootstrapped Information Extraction
We challenge a common assumption in active learning, that a list-based interface populated by informative samples provides for efficient and effective data annotation.
Keep Your Bearings: Lightly-Supervised Information Extraction with Ladder Networks That Avoids Semantic Drift
We propose a novel approach to semi-supervised learning for information extraction that uses ladder networks (Rasmus et al., 2015).
An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classification
Several semi-supervised representation learning methods have been proposed recently that mitigate the drawbacks of traditional bootstrapping: they reduce the amount of semantic drift introduced by iterative approaches through one-shot learning; others address the sparsity of data through the learning of custom, dense representation for the information modeled.
Exploration of Noise Strategies in Semi-supervised Named Entity Classification
Our work aims to address this gap by exploring different noise strategies for the semi-supervised named entity classification task, including statistical methods such as adding Gaussian noise to input embeddings, and linguistically-inspired ones such as dropping words and replacing words with their synonyms.
Semi-Supervised Teacher-Student Architecture for Relation Extraction
Generating a large amount of training data for information extraction (IE) is either costly (if annotations are created manually), or runs the risk of introducing noisy instances (if distant supervision is used).
Parallel Corpus Filtering via Pre-trained Language Models
Web-crawled data provides a good source of parallel corpora for training machine translation models.
Ranked #1 on
Machine Translation
on WMT2019 English-Japanese
FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN
The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2020) featured this year six challenge tracks: (i) Simultaneous speech translation, (ii) Video speech translation, (iii) Offline speech translation, (iv) Conversational speech translation, (v) Open domain translation, and (vi) Non-native speech translation.
Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play
Task-oriented dialog systems are often trained on human/human dialogs, such as collected from Wizard-of-Oz interfaces.
MeetDot: Videoconferencing with Live Translation Captions
We use the re-translation strategy to translate the streamed speech, resulting in caption flicker.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Findings of the 2021 Conference on Machine Translation (WMT21)
This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021. In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories.
