Search Results for author:
Found 14 papers, 3 papers with code
BIT-Xiaomi’s System for AutoSimTrans 2022
This system paper describes the BIT-Xiaomi simultaneous translation system for Autosimtrans 2022 simultaneous translation challenge.
CBSiMT: Mitigating Hallucination in Simultaneous Machine Translation with Weighted Prefix-to-Prefix Training
Simultaneous machine translation (SiMT) is a challenging task that requires starting translation before the full source sentence is available.
From Indeterminacy to Determinacy: Augmenting Logical Reasoning Capabilities with Large Language Models
To address these challenges, we propose DetermLR, a novel reasoning framework that formulates the reasoning process as a transformational journey from indeterminate premises to determinate ones.
CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?
We present the Chinese Elementary School Math Word Problems (CMATH) dataset, comprising 1. 7k elementary school-level math word problems with detailed annotations, source from actual Chinese workbooks and exams.
UniMC: A Unified Framework for Long-Term Memory Conversation via Relevance Representation Learning
In this paper, we propose a Unified framework for Long-term Memory Conversations (UniMC), which increases the connection between different stages by learning relevance representation.
Exploring Better Text Image Translation with Multimodal Codebook
Text image translation (TIT) aims to translate the source texts embedded in the image to target translations, which has a wide range of applications and thus has important research value.
Rethinking the Reasonability of the Test Set for Simultaneous Machine Translation
Simultaneous machine translation (SimulMT) models start translation before the end of the source sentence, making the translation monotonically aligned with the source sentence.
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation
Accordingly, we propose a novel paradigm, i. e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy.
Improve Bilingual TTS Using Dynamic Language and Phonology Embedding
We introduce phonology embedding to capture the English differences between different phonology.
PAMA-TTS: Progression-Aware Monotonic Attention for Stable Seq2Seq TTS With Accurate Phoneme Duration Control
Sequence expansion between encoder and decoder is a critical challenge in sequence-to-sequence TTS.
HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis
To tackle the difficulty of singing modeling caused by high sampling rate (wider frequency band and longer waveform), we introduce multi-scale adversarial training in both the acoustic model and vocoder to improve singing modeling.
DeepSinger: Singing Voice Synthesis with Data Mined From the Web
DeepSinger has several advantages over previous SVS systems: 1) to the best of our knowledge, it is the first SVS system that directly mines training data from music websites, 2) the lyrics-to-singing alignment model further avoids any human efforts for alignment labeling and greatly reduces labeling cost, 3) the singing model based on a feed-forward Transformer is simple and efficient, by removing the complicated acoustic feature modeling in parametric synthesis and leveraging a reference encoder to capture the timbre of a singer from noisy singing data, and 4) it can synthesize singing voices in multiple languages and multiple singers.
Adversarially Trained Multi-Singer Sequence-To-Sequence Singing Synthesizer
This paper presents a high quality singing synthesizer that is able to model a voice with limited available recordings.
XiaoiceSing: A High-Quality and Integrated Singing Voice Synthesis System
This paper presents XiaoiceSing, a high-quality singing voice synthesis system which employs an integrated network for spectrum, F0 and duration modeling.
