870 Results
August 1, 2019
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue
Annual Meeting of the Association for Computational Linguistics (ACL)
In this paper, we (1) propose using tree-structured semantic representations, like those used in traditional rule-based NLG systems, for better discourse-level structuring and sentence-level planning; (2) introduce a challenging dataset using this representation for the weather domain; (3) introduce a constrained decoding approach for Seq2Seq models that leverages this representation to improve semantic correctness; and (4) demonstrate promising results on our dataset and the E2E dataset.
By: Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba
Machine Learning 
Natural Language Processing & Speech August 1, 2019
Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification
Workshop on Abusive Language Online
We present a number of fusion approaches to integrate text and photo signals. We show that augmenting text with image embedding information immediately leads to a boost in performance, while applying additional attention fusion methods brings further improvement.
By: Fan Yang, Xiaochang Peng, Gargi Ghosh, Reshef Shilon, Hao Ma, Eider Moore, Goran Predovic
Facebook AI Research July 31, 2019
Neural Volumes: Learning Dynamic Renderable Volumes from Images
SIGGRAPH
To overcome memory limitations of voxel-based representations, we learn a dynamic irregular grid structure implemented with a warp field during ray-marching. This structure greatly improves the apparent resolution and reduces grid-like artifacts and jagged motion. Finally, we demonstrate how to incorporate surface-based representations into our volumetric-learning framework for applications where the highest resolution is required, using facial performance capture as a case in point.
By: Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, Yaser Sheikh
AR/VR 
Computer Vision July 29, 2019
Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
Association for Computational Linguistics (ACL)
One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill. While it is straightforward for humans to recognize and acknowledge others’ feelings in a conversation, this is a significant challenge for AI systems due to the paucity of suitable publicly-available datasets for training and evaluation. This work proposes a new benchmark for empathetic dialogue generation and EMPATHETICDIALOGUES, a novel dataset of 25k conversations grounded in emotional situations.
By: Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan BoureauJuly 29, 2019
Better Character Language Modeling Through Morphology
Association for Computational Linguistics (ACL)
We incorporate morphological supervision into character language models (CLMs) via multitasking and show that this addition improves bits-per-character (BPC) performance across 24 languages, even when the morphology data and language modeling data are disjoint.
By: Terra Blevins, Luke ZettlemoyerJuly 29, 2019
Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations
Association for Computational Linguistics (ACL)
Zero-shot translation, translating between language pairs on which a Neural Machine Translation (NMT) system has never been trained, is an emergent property when training the system in multilingual settings. However, naïve training for zero-shot NMT easily fails, and is sensitive to hyper-parameter setting. The performance typically lags far behind the more conventional pivot-based approach which translates twice using a third language as a pivot.
By: Jiatao Gu, Yong Wang, Kyunghyun Cho, Victor O.K. LiJuly 29, 2019
Scaling Static Analyses at Facebook
Communications of the ACM (CACM)
Static analysis tools are programs that examine, and attempt to draw conclusions about, the source of other programs, without running them. At Facebook we have been investing in advanced static analysis tools that employ reasoning techniques similar to those from program verification.
By: Dino Distefano, Manuel Fahndrich, Francesco Logozzo, Peter O'Hearn
Systems & Networking July 29, 2019
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
To address the challenge of rapidly generating low-level controllers, we argue for using model-based reinforcement learning (MBRL) trained on relatively small amounts of automatically generated (i.e., without system simulation) data. In this paper, we explore the capabilities of MBRL on a Crazyflie centimeter-scale quadrotor with rapid dynamics to predict and control at ≤ 50Hz.
By: Nathan O. Lambert, Daniel S. Drew, Joseph Yaconelli, Sergey Levine, Roberto Calandra, Kristofer S. J. PisterJuly 29, 2019
Word-order biases in deep-agent emergent communication
Association for Computational Linguistics (ACL)
Sequence-processing neural networks led to remarkable progress on many NLP tasks. As a consequence, there has been increasing interest in understanding to what extent they process language as humans do. We aim here to uncover which biases such models display with respect to “natural” word-order constraints.
By: Rahma Chaabouni, Eugene Kharitonov, Alessandro Lazaric, Emmanuel Dupoux, Marco BaroniJuly 29, 2019
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs
Association for Computational Linguistics (ACL)
We study a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph (KG) to introduce engaging and contextually diverse entities and attributes. For this study, we collect a new Open-ended Dialog ↔ KG parallel corpus called OpenDialKG, where each utterance from 15K human-to-human role-playing dialogs is manually annotated with ground-truth reference to corresponding entities and paths from a large-scale KG with 1M+ facts.
By: Shane Moon, Pararth Shah, Anuj Kumar, Rajen Subba