Rethinking Skip-thought: A Neighborhood based Approach
We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as a neighborhood. We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks. Both quantitative comparison and qualitative investigation are conducted. We empirically show that, our skip-thought neighbor model performs as well as the skip-thought model on evaluation tasks. In addition, we found that, incorporating an autoencoder path in our model didn't aid our model to perform better, while it hurts the performance of the skip-thought model.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Skip-Thought Memory Networks
Question Answering (QA) is fundamental to natural language processing in that most nlp problems can be phrased as QA (Kumar et al., 2015). Current weakly supervised memory network models that have been proposed so far st…
Question AnsweringSentenceTrimming and Improving Skip-thought Vectors
The skip-thought model has been proven to be effective at learning sentence representations and capturing sentence semantics. In this paper, we propose a suite of techniques to trim and improve it. First, we validate a h…
DecoderSentencetext-classificationText ClassificationTakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors
This paper describes our system for the SemEval-2018 Task 12: Argument Reasoning Comprehension Task. We utilize skip-thought vectors, sentence-level distributional vectors inspired by the popular word embeddings and the …
Common Sense ReasoningNatural Language InferenceSentenceWord EmbeddingsRethinking Skip Connections in Encoder-decoder Networks for Monocular Depth Estimation
Skip connections are fundamental units in encoder-decoder networks, which are able to improve the feature propagtion of the neural networks. However, most methods with skip connections just connected features with the sa…
DecoderDepth EstimationMonocular Depth EstimationSkip-Thought GAN: Generating Text through Adversarial Training using Skip-Thought Vectors
In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of ta…
Image GenerationSentenceSentence EmbeddingsStyle Transfer+2