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Found 20 papers, 1 papers with code
Checkpoint Reranking: An Approach to Select Better Hypothesis for Neural Machine Translation Systems
After training a Neural Machine Translation (NMT) baseline system, it has been observed that these iteration outputs have an oracle score higher than baseline up to 1. 01 BLEU points compared to the last iteration of the trained system. We come up with a ranking mechanism by solely focusing on the decoder{'}s ability to generate distinct tokens and without the usage of any language model or data.
CodeForTheChange at SemEval-2019 Task 8: Skip-Thoughts for Fact Checking in Community Question Answering
The strengths of the scalable gradient tree boosting algorithm, XGBoost and distributed sentence encoder, Skip-Thought Vectors are not explored yet by the cQA research community.
Adaptive Artificial Intelligent Q&A Platform
The paper presents an approach to build a question and answer system that is capable of processing the information in a large dataset and allows the user to gain knowledge from this dataset by asking questions in natural language form.
Hunter NMT System for WMT18 Biomedical Translation Task: Transfer Learning in Neural Machine Translation
Furthermore, we applied ensemble learning on training models of intermediate epochs and achieved an improvement of 4. 02 BLEU points over the baseline.
CNN for Text-Based Multiple Choice Question Answering
The task of Question Answering is at the very core of machine comprehension.
Exemplar Encoder-Decoder for Neural Conversation Generation
In this paper we present the Exemplar Encoder-Decoder network (EED), a novel conversation model that learns to utilize \textit{similar} examples from training data to generate responses.
Oral-Motor and Lexical Diversity During Naturalistic Conversations in Adults with Autism Spectrum Disorder
Computational studies of language in ASD provide support for the existence of an underlying dimension of restriction that emerges during a conversation.
Injecting Word Embeddings with Another Language's Resource : An Application of Bilingual Embeddings
Word embeddings learned from text corpus can be improved by injecting knowledge from external resources, while at the same time also specializing them for similarity or relatedness.
Effective search space reduction for spell correction using character neural embeddings
We present a novel, unsupervised, and distance measure agnostic method for search space reduction in spell correction using neural character embeddings.
Dealing with Linguistic Divergences in English-Bhojpuri Machine Translation
Furthermore, so far as the methodology is concerned, we have adhered to the Dorr{'}s Lexical Conceptual Structure for the resolution of divergences.
