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Found 18 papers, 1 papers with code
RoundTable: Investigating Group Decision-Making Mechanism in Multi-Agent Collaboration
This study investigates the efficacy of Multi-Agent Systems in eliciting cross-agent communication and enhancing collective intelligence through group decision-making in a decentralized setting.
Eliciting Better Multilingual Structured Reasoning from LLMs through Code
xSTREET exposes a gap in base LLM performance between English and non-English reasoning tasks.
Dialog2API: Task-Oriented Dialogue with API Description and Example Programs
The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs.
Start-Before-End and End-to-End: Neural Speech Translation by AppTek and RWTH Aachen University
AppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Neural Simultaneous Speech Translation Using Alignment-Based Chunking
In simultaneous machine translation, the objective is to determine when to produce a partial translation given a continuous stream of source words, with a trade-off between latency and quality.
On The Alignment Problem In Multi-Head Attention-Based Neural Machine Translation
This work investigates the alignment problem in state-of-the-art multi-head attention models based on the transformer architecture.
Neural Hidden Markov Model for Machine Translation
Attention-based neural machine translation (NMT) models selectively focus on specific source positions to produce a translation, which brings significant improvements over pure encoder-decoder sequence-to-sequence models.
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
We compare the fast training and decoding speed of RETURNN of attention models for translation, due to fast CUDA LSTM kernels, and a fast pure TensorFlow beam search decoder.
Hybrid Neural Network Alignment and Lexicon Model in Direct HMM for Statistical Machine Translation
Recently, the neural machine translation systems showed their promising performance and surpassed the phrase-based systems for most translation tasks.
