815 Results
January 1, 2020
Designing Safe Spaces for Virtual Reality
Ethics in Design and Communication
Virtual Reality (VR) designers accept the ethical responsibilities of removing a user’s entire world and superseding it with a fabricated reality. These unique immersive design challenges are intensified when virtual experiences become public and socially-driven. As female VR designers in 2018, we see an opportunity to fold the language of consent into the design practice of virtual reality—as a means to design safe, accessible, virtual spaces.
Publication will be made available in 2020.
By: Michelle Cortese, Andrea Zeller
AR/VR 
Human Computer Interaction & UX August 1, 2019
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
Annual Meeting of the Association for Computational Linguistics (ACL)
This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty.
By: Yi Tay, Shuohang Wang, Luu Anh Tuan, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang
Facebook AI Research 
Machine Learning 
Natural Language Processing & Speech 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 SubbaAugust 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 PredovicAugust 1, 2019
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks
Annual Meeting of the Association for Computational Linguistics (ACL)
Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks.
By: Yi Tay, Aston Zhang, Luu Anh Tuan, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, Siu Cheung HuiJuly 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
Computer Vision July 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
Keeping Notes: Conditional Natural Language Generation with a Scratchpad Mechanism
Association for Computational Linguistics (ACL)
We introduce the Scratchpad Mechanism, a novel addition to the sequence-to-sequence (seq2seq) neural network architecture and demonstrate its effectiveness in improving the overall fluency of seq2seq models for natural language generation tasks.
By: Ryan Y. Benmalek, Madian Khabsa, Suma Desu, Claire Cardie, Michele BankoJuly 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 28, 2019
What makes a good conversation? How controllable attributes affect human judgments
North American Chapter of the Association for Computational Linguistics (NAACL)
In this work, we examine two controllable neural text generation methods, conditional training and weighted decoding, in order to control four important attributes for chitchat dialogue: repetition, specificity, response-relatedness and question-asking.
By: Abigail See, Stephen Roller, Douwe Kiela, Jason Weston