Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning

We introduce a novel task, zero-shot video captioning, that aims at describing out-of-domain videos of unseen activities. We propose a principled Topic-Aware Mixture of Experts (TAMoE) model for zero-shot video captioning, which learns to compose different experts based on different topic embeddings, implicitly transferring the knowledge learned from seen activities to unseen ones (AAAI 2019).
[Code]   [Paper]  

One-Shot Relational Learning for Knowledge Graphs

We aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge extracted by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures (EMNLP 2018).
[Code]   [Paper]  

WikiHow: A Large Scale Text Summarization Dataset

We present WikiHow, a dataset of more than 230,000 article and summary pairs extracted and constructed from an online knowledge base written by different human authors. The articles span a wide range of topics and therefore represent high diversity styles.
[Data]   [Paper]  

XL-NBT: A Cross-lingual Neural Belief Tracking Framework

With globalization, the need for providing cross-lingual customer support becomes more urgent than ever. However, cross-lingual support poses great challenges - it requires a large amount of additional annotated data from native speakers. In order to bypass the expensive human annotation and achieve the first step towards the ultimate goal of building a universal dialog system, we set out to build a cross-lingual state tracking framework (EMNLP 2018).
[Code]   [Paper]  

Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

We propose a novel scheduled policy optimization mechanism which dynamically schedules demonstration learning and reinforcement learning and addresses the discrepancy between training and inference in sequence decoding (IJCAI-ECAI 2018).
[Code]   [Paper]  

MojiTalk: Generating Emotional Responses at Scale

We exploit the generating emotional language of leveraging Twitter data that are naturally labeled with emojis. We investigate several conditional variational autoencoders training on conversations, which allow us to use emojis to control the emotion of the generated text (ACL 2018).
[Code]   [Paper]  

No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling

We propose an Adversarial REward Learning (AREL) framework to learn an implicit reward function from human demonstrations, and then optimize policy search with the learned reward function (ACL 2018).
[Code]   [Paper]  

Simple Models for Word Formation in English Slang

We propose generative models for three types of extra-grammatical word formation phenomena abounding in English slang: Blends, Clippings, and Reduplicatives (NAACL-HLT 2018).
[Code]   [Paper]  

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models (NAACL-HLT 2018).
[Code]   [Paper]  

DeepPath: Reinforcement Learning for Knowledge Graph Reasoning

We describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path (EMNLP 2017).
[Code]   [Paper]  

Deep Residual Learning for Weakly-Supervised Relation Extraction

We design a novel convolutional neural network (CNN) with residual learning, and investigate its impacts on the task of distantly supervised noisy relation extraction (EMNLP 2017).
[Code]   [Paper]