Search Results for author:
Found 14 papers, 6 papers with code
JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions
Hence, in order to achieve higher performance on our tasks, models need to effectively utilize such functional knowledge to infer the outcomes of actions, rather than relying solely on memorizing facts.
Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference
We introduce a neuro-symbolic natural logic framework based on reinforcement learning with introspective revision.
GRN: Generative Rerank Network for Context-wise Recommendation
Specifically, we first design the evaluator, which applies Bi-LSTM and self-attention mechanism to model the contextual information in the labeled final ranking list and predict the interaction probability of each item more precisely.
Complementary Evidence Identification in Open-Domain Question Answering
This paper proposes a new problem of complementary evidence identification for open-domain question answering (QA).
Revisit Recommender System in the Permutation Prospective
Afterwards, for the candidate list set, the PRank stage provides a unified permutation-wise ranking criterion named LR metric, which is calculated by the rating scores of elaborately designed permutation-wise model DPWN.
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems
Task-oriented conversational modeling with unstructured knowledge access, as track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to build a system to generate response given dialogue history and knowledge access.
Exploring End-to-End Differentiable Natural Logic Modeling
We explore end-to-end trained differentiable models that integrate natural logic with neural networks, aiming to keep the backbone of natural language reasoning based on the natural logic formalism while introducing subsymbolic vector representations and neural components.
Deriving Commonsense Inference Tasks from Interactive Fictions
Commonsense reasoning simulates the human ability to make presumptions about our physical world, and it is an indispensable cornerstone in building general AI systems.
Program Enhanced Fact Verification with Verbalization and Graph Attention Network
Built on that, we construct the graph attention verification networks, which are designed to fuse different sources of evidences from verbalized program execution, program structures, and the original statements and tables, to make the final verification decision.
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction.
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph.
EdgeRec: Recommender System on Edge in Mobile Taobao
Recommender system (RS) has become a crucial module in most web-scale applications.
Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games
We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i. e., the question-answer pairs.
Deep Session Interest Network for Click-Through Rate Prediction
Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt, Deep Session Interest Network(DSIN)
