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Found 11 papers, 5 papers with code
Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering
Our method reaches state-of-the-art performance on 5 benchmark QA datasets, with up to 10% improvement in top-100 accuracy compared to a joint-training multi-task DPR on SQuAD.
JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games
This paper presents an empirical exploration of non-transitivity in perfect-information games, specifically focusing on Xiangqi, a traditional Chinese board game comparable in game-tree complexity to chess and shogi.
Don't Change Me! User-Controllable Selective Paraphrase Generation
The contribution of this work is a novel data generation technique using distant supervision that allows us to start with a pretrained sequence-to-sequence model and fine-tune a paraphrase generator that exhibits this behavior, allowing user-controllable paraphrase generation.
Segatron: Segment-Aware Transformer for Language Modeling and Understanding
To verify this, we propose a segment-aware Transformer (Segatron), by replacing the original token position encoding with a combined position encoding of paragraph, sentence, and token.
Ranked #20 on
Language Modelling
on WikiText-103
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2
Experimental results show that the Chinese GPT2 can generate better essay endings with \eop.
Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
Techniques for automatically extracting important content elements from business documents such as contracts, statements, and filings have the potential to make business operations more efficient.
End-to-End Neural Context Reconstruction in Chinese Dialogue
We tackle the problem of context reconstruction in Chinese dialogue, where the task is to replace pronouns, zero pronouns, and other referring expressions with their referent nouns so that sentences can be processed in isolation without context.
Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features
Consumers dissatisfied with the normal dispute resolution process provided by an e-commerce company{'}s customer service agents have the option of escalating their complaints by filing grievances with a government authority.
Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering
Recently, a simple combination of passage retrieval using off-the-shelf IR techniques and a BERT reader was found to be very effective for question answering directly on Wikipedia, yielding a large improvement over the previous state of the art on a standard benchmark dataset.
Ranked #3 on
Open-Domain Question Answering
on SQuAD1.1 dev
End-to-End Open-Domain Question Answering with BERTserini
We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit.
Ranked #4 on
Open-Domain Question Answering
on SQuAD1.1 dev
Neural Contextual Conversation Learning with Labeled Question-Answering Pairs
The novel contextual model generates diverse and robust responses, and is able to carry out conversations on a wide range of topics appropriately.
