output
20152019
most citedBag of Freebies for Training Object Detection Neural Networks

147 citations

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9 papers · 1 filter

cs.CL20194 cited

Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation

Nima Pourdamghani, Nada Aldarrab, Marjan Ghazvininejad +2

Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore…

cs.CL20192 cited

Improving Long Distance Slot Carryover in Spoken Dialogue Systems

Tongfei Chen, Chetan Naik, Hua He +2

Tracking the state of the conversation is a central component in task-oriented spoken dialogue systems. One such approach for tracking the dialogue state is slot carryover, where a…

cs.CL20199 cited

Data Selection with Cluster-Based Language Difference Models and Cynical Selection

Lucía Santamaría, Amittai Axelrod

We present and apply two methods for addressing the problem of selecting relevant training data out of a general pool for use in tasks such as machine translation. Building on exis…

cs.CL2019

Cross-lingual transfer learning for spoken language understanding

Quynh Ngoc Thi Do, Judith Gaspers

Typically, spoken language understanding (SLU) models are trained on annotated data which are costly to gather. Aiming to reduce data needs for bootstrapping a SLU system for a new…

cs.CL2019

Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering

Fréderic Godin, Anjishnu Kumar, Arpit Mittal

In this paper, we investigate the challenges of using reinforcement learning agents for question-answering over knowledge graphs for real-world applications. We examine the perform…

cs.CL20194 cited

A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)

Michael Regan, Pushpendre Rastogi, Arpit Gupta +1

We present Contextual Query Rewrite (CQR) a dataset for multi-domain task-oriented spoken dialogue systems that is an extension of the Stanford dialog corpus (Eric et al., 2017a).…