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Found 14 papers, 8 papers with code
Generative Dense Retrieval: Memory Can Be a Burden
Memorizing-free matching mechanism from Dense Retrieval (DR) is then introduced to conduct fine-grained intra-cluster matching from clusters to relevant documents.
BatchEval: Towards Human-like Text Evaluation
Significant progress has been made in automatic text evaluation with the introduction of large language models (LLMs) as evaluators.
Turning Dust into Gold: Distilling Complex Reasoning Capabilities from LLMs by Leveraging Negative Data
In this work, we illustrate the merit of negative data and propose a model specialization framework to distill LLMs with negative samples besides positive ones.
TopNet: Learning from Neural Topic Model to Generate Long Stories
In particular, instead of directly generating a story, we first learn to map the short text input to a low-dimensional topic distribution (which is pre-assigned by a topic model).
Adversarial Mutual Information for Text Generation
Recent advances in maximizing mutual information (MI) between the source and target have demonstrated its effectiveness in text generation.
Towards Evaluating the Robustness of Chinese BERT Classifiers
Recent advances in large-scale language representation models such as BERT have improved the state-of-the-art performances in many NLP tasks.
Bi-Decoder Augmented Network for Neural Machine Translation
Neural Machine Translation (NMT) has become a popular technology in recent years, and the encoder-decoder framework is the mainstream among all the methods.
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
In particular, we propose a tree-based autoencoder to embed the discrete text data into a continuous representation space, upon which we optimize the adversarial perturbation.
Reinforced Dynamic Reasoning for Conversational Question Generation
This paper investigates a new task named Conversational Question Generation (CQG) which is to generate a question based on a passage and a conversation history (i. e., previous turns of question-answer pairs).
MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models
In this paper, we transfer knowledge learned from machine comprehension to the sequence-to-sequence tasks to deepen the understanding of the text.
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference
We observe that people usually use some discourse markers such as "so" or "but" to represent the logical relationship between two sentences.
Ranked #13 on
Natural Language Inference
on SNLI
Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation
In this paper, we propose a novel neural network system that consists a Demand Optimization Model based on a passage-attention neural machine translation and a Reader Model that can find the answer given the optimized question.
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension
Machine comprehension(MC) style question answering is a representative problem in natural language processing.
Ranked #37 on
Question Answering
on TriviaQA
