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Found 19 papers, 6 papers with code
IIE’s Neural Machine Translation Systems for WMT20
In this paper we introduce the systems IIE submitted for the WMT20 shared task on German-French news translation.
Psychology-guided Controllable Story Generation
Inspired by psychology theories, we introduce global psychological state chains, which include the needs and emotions of the protagonists, to help a story generation system create more controllable and well-planned stories.
COMMA: Modeling Relationship among Motivations, Emotions and Actions in Language-based Human Activities
Motivations, emotions, and actions are inter-related essential factors in human activities.
Do You Know My Emotion? Emotion-Aware Strategy Recognition towards a Persuasive Dialogue System
Specifically, CFO-Net designs a feedback memory module, including strategy pool and feedback pool, to obtain emotion-aware strategy representation.
CogIntAc: Modeling the Relationships between Intention, Emotion and Action in Interactive Process from Cognitive Perspective
In this paper, we propose a novel cognitive framework of individual interaction.
Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversation
Emotional support conversation aims at reducing the emotional distress of the help-seeker, which is a new and challenging task.
Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval
We find that the performance of retrieval models trained on dataset from general domain will inevitably decrease on specific domain.
CLSEG: Contrastive Learning of Story Ending Generation
To address these two issues, we propose a novel Contrastive Learning framework for Story Ending Generation (CLSEG), which has two steps: multi-aspect sampling and story-specific contrastive learning.
Modeling Intention, Emotion and External World in Dialogue Systems
Intention, emotion and action are important elements in human activities.
Know Deeper: Knowledge-Conversation Cyclic Utilization Mechanism for Open-domain Dialogue Generation
End-to-End intelligent neural dialogue systems suffer from the problems of generating inconsistent and repetitive responses.
Coarse-to-Careful: Seeking Semantic-related Knowledge for Open-domain Commonsense Question Answering
It is prevalent to utilize external knowledge to help machine answer questions that need background commonsense, which faces a problem that unlimited knowledge will transmit noisy and misleading information.
MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension
Most of the existing systems design a simple classifier to determine answerability implicitly without explicitly modeling mutual interaction and relation between the question and passage, leading to the poor performance for determining the unanswerable questions.
IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation
This paper introduces our systems for all three subtasks of SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning.
Bi-directional CognitiveThinking Network for Machine Reading Comprehension
We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory.
Bi-directional Cognitive Thinking Network for Machine Reading Comprehension
We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory.
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation
As a sequence-to-sequence generation task, neural machine translation (NMT) naturally contains intrinsic uncertainty, where a single sentence in one language has multiple valid counterparts in the other.
On Learning Universal Representations Across Languages
Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks.
Contrastive Learning
Cross-Lingual Natural Language Inference
+4
IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE
This paper introduces our systems for the first two subtasks of SemEval Task4: Commonsense Validation and Explanation.
Unsupervised Neural Machine Translation with Future Rewarding
In this paper, we alleviate the local optimality of back-translation by learning a policy (takes the form of an encoder-decoder and is defined by its parameters) with future rewarding under the reinforcement learning framework, which aims to optimize the global word predictions for unsupervised neural machine translation.
