Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
Analyzing people's opinions and sentiments towards certain aspects is an important task of
natural language understanding. In this paper, we propose a novel solution to targeted …
natural language understanding. In this paper, we propose a novel solution to targeted …
A deep look into neural ranking models for information retrieval
Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …
decades, different techniques have been proposed for constructing ranking models, from …
Adversarial learning for neural dialogue generation
In this paper, drawing intuition from the Turing test, we propose using adversarial training for
open-domain dialogue generation: the system is trained to produce sequences that are …
open-domain dialogue generation: the system is trained to produce sequences that are …
Deep reinforcement learning for dialogue generation
Recent neural models of dialogue generation offer great promise for generating responses
for conversational agents, but tend to be shortsighted, predicting utterances one at a time …
for conversational agents, but tend to be shortsighted, predicting utterances one at a time …
Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots
We study response selection for multi-turn conversation in retrieval-based chatbots. Existing
work either concatenates utterances in context or matches a response with a highly abstract …
work either concatenates utterances in context or matches a response with a highly abstract …
Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis
Sentiment analysis has emerged as one of the most popular natural language processing
(NLP) tasks in recent years. A classic setting of the task mainly involves classifying the …
(NLP) tasks in recent years. A classic setting of the task mainly involves classifying the …
Commonsense for generative multi-hop question answering tasks
Reading comprehension QA tasks have seen a recent surge in popularity, yet most works
have focused on fact-finding extractive QA. We instead focus on a more challenging multi …
have focused on fact-finding extractive QA. We instead focus on a more challenging multi …
Speaker-aware BERT for multi-turn response selection in retrieval-based chatbots
In this paper, we study the problem of employing pre-trained language models for multi-turn
response selection in retrieval-based chatbots. A new model, named Speaker-Aware BERT …
response selection in retrieval-based chatbots. A new model, named Speaker-Aware BERT …
Grounded conversation generation as guided traverses in commonsense knowledge graphs
Human conversations naturally evolve around related concepts and scatter to multi-hop
concepts. This paper presents a new conversation generation model, ConceptFlow, which …
concepts. This paper presents a new conversation generation model, ConceptFlow, which …
One time of interaction may not be enough: Go deep with an interaction-over-interaction network for response selection in dialogues
Currently, researchers have paid great attention to retrieval-based dialogues in open-
domain. In particular, people study the problem by investigating context-response matching …
domain. In particular, people study the problem by investigating context-response matching …