Qun Liu

$gen$CNN: A Convolutional Architecture for Word Sequence Prediction

2015 · arXiv (Cornell University)

We propose a novel convolutional architecture, named $gen$CNN, for word sequence prediction. Different from previous work on neural network-based language modeling and generation (e.g., RNN or LSTM), we choose not to greedily summarize the history …

ADAPT Centre Cone Team at IJCNLP-2017 Task 5: A Similarity-Based Logistic Regression Approach to Multi-choice Question Answering in an Examinations Shared Task

2017 · International Joint Conference on Natural Language Processing

We describe the work of a team from the ADAPT Centre in Ireland in addressing automatic answer selection for the Multi-choice Question Answering in Examinations shared task. The system is based on a logistic regression …

Bilingual-GAN: A Step Towards Parallel Text Generation

2019 · arXiv (Cornell University)

Latent space based GAN methods and attention based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respectively. Leveraging the two domains, we propose an adversarial latent space based …

From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables

2022 · Proceedings of the AAAI Conference on Artificial Intelligence

Embedding matrices are key components in neural natural language processing (NLP) models that are responsible to provide numerical representations of input tokens (i.e. words or subwords). In this paper, we analyze the impact and utility …

Enhancing implicit sentiment analysis via knowledge enhancement and context information

2025 · Complex & Intelligent Systems

Sentiment analysis (SA) is a vital research direction in natural language processing (NLP). Compared with the widely-concerned explicit sentiment analysis, implicit sentiment analysis (ISA) is more challenging and rarely studied due to the lack of …

Findings of the 2017 Conference on Machine Translation (WMT17)

2017

Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Shujian Huang, Matthias Huck, Philipp Koehn, Qun Liu, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Raphael Rubino, Lucia Specia, Marco Turchi. Proceedings of the …

Knowledge Diffusion for Neural Dialogue Generation

2018

End-to-end neural dialogue generation has shown promising results recently, but it does not employ knowledge to guide the generation and hence tends to generate short, general, and meaningless responses. In this paper, we propose a …

Decomposable Neural Paraphrase Generation

2019

Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level. This paper presents Decomposable Neural Paraphrase Generator (DNPG), a Transformer-based model that can learn and generate paraphrases of a sentence …

ERNIE: Enhanced Language Representation with Informative Entities

2019

Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks. However, the existing …

Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search

2017

We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model that generates a sequence = {y 0 …

Bridging the Gap between Training and Inference for Neural Machine Translation

2019

Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words. At training time, it predicts with the ground truth words as context while at …

TinyBERT: Distilling BERT for Natural Language Understanding

2020

Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationally expensive, so it is difficult to efficiently execute them on resourcerestricted …