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Found 13 papers, 4 papers with code
Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings
Neural Machine Translation (NMT) has shown a strong ability to utilize local context to disambiguate the meaning of words.
Huawei AARC’s Submissions to the WMT21 Biomedical Translation Task: Domain Adaption from a Practical Perspective
This paper describes Huawei Artificial Intelligence Application Research Center’s neural machine translation systems and submissions to the WMT21 biomedical translation shared task.
Cross-lingual Feature Extraction from Monolingual Corpora for Low-resource Unsupervised Bilingual Lexicon Induction
Despite their progress in high-resource language settings, unsupervised bilingual lexicon induction (UBLI) models often fail on corpora with low-resource distant language pairs due to insufficient initialization.
Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain
We expect that agents should learn to enhance the extent to which humans achieve these goals while maintaining agents' original abilities (e. g., winning games).
Retrieval-augmented Multilingual Knowledge Editing
Knowledge represented in Large Language Models (LLMs) is quite often incorrect and can also become obsolete over time.
Assessing the Reliability of Large Language Model Knowledge
Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks.
Hermes: Unlocking Security Analysis of Cellular Network Protocols by Synthesizing Finite State Machines from Natural Language Specifications
In this paper, we present Hermes, an end-to-end framework to automatically generate formal representations from natural language cellular specifications.
Evaluating the Efficacy of Length-Controllable Machine Translation
We can use automatic summarization or machine translation evaluation metrics for length-controllable machine translation, but this is not necessarily suitable and accurate.
Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective
MOBA games, e. g., Dota2 and Honor of Kings, have been actively used as the testbed for the recent AI research on games, and various AI systems have been developed at the human level so far.
Learning Homographic Disambiguation Representation for Neural Machine Translation
Visualization methods like heatmaps, T-SNE and translation examples are also utilized to demonstrate the effects of the proposed method.
Positively transitioned sentiment dialogue corpus for developing emotion-affective open-domain chatbots
In this paper, we describe a data enhancement method for developing Emily, an emotion-affective open-domain chatbot.
Learning Diverse Policies in MOBA Games via Macro-Goals
Recently, many researchers have made successful progress in building the AI systems for MOBA-game-playing with deep reinforcement learning, such as on Dota 2 and Honor of Kings.
Emily: Developing An Emotion-affective Open-Domain Chatbot with Knowledge Graph-based Persona
In this paper, we describe approaches for developing Emily, an emotion-affective open-domain chatbot.
