In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
--------
Question: We present a simple idea that allows to record a speaker in a given language and synthesize their voice in other languages that they may not even know. These techniques open a wide range of potential applications such as cross-language communication, language learning or automatic video dubbing. We call this general problem multi-language speaker-conditioned speech synthesis and we present a simple but strong baseline for it.

 Our model architecture is similar to the encoder-decoder Char2Wav model or Tacotron. The main difference is that, instead of conditioning on characters or phonemes that are specific to a given language, we condition on a shared phonetic representation that is universal to all languages. This cross-language phonetic representation of text allows to synthesize speech in any language while preserving the vocal characteristics of the original speaker. Furthermore, we show that fine-tuning the weights of our model allows us to extend our results to speakers outside of the training dataset.

Answer: We present a simple idea that allows to record a speaker in a given language and synthesize their voice in other languages that they may not even know.


Question: Different kinds of representation learning techniques on graph have shown significant effect in downstream machine learning tasks. Recently, in order to inductively learn representations for graph structures that is unobservable during training, a general framework with sampling and aggregating (GraphSAGE) was proposed by Hamilton and Ying and had been proved more efficient than transductive methods on fileds like transfer learning or evolving dataset. However, GraphSAGE is uncapable of selective neighbor sampling and lack of memory of known nodes that've been trained. To address these problems, we present an unsupervised method that samples neighborhood information attended by co-occurring structures and optimizes a trainable global bias as a representation expectation for each node in the given graph. Experiments show that our approach outperforms the state-of-the-art inductive and unsupervised methods for representation learning on graphs.

Answer: For unsupervised and inductive network embedding, we propose a novel approach to explore most relevant neighbors and preserve previously learnt knowledge of nodes by utilizing bi-attention architecture and introducing global bias, respectively


Question: This paper revisits the problem of sequence modeling using convolutional 
 architectures.  Although both convolutional and recurrent architectures have a
long history in sequence prediction, the current "default" mindset in much of
 the deep learning community is that generic sequence modeling is best handled
 using recurrent networks.  The goal of this paper is to question this assumption . 
Specifically, we consider a simple generic temporal convolution network (TCN ),
which adopts features from modern ConvNet architectures such as a dilations and 
 residual connections.  We show that on a variety of sequence modeling tasks ,
including many frequently used as benchmarks for evaluating recurrent networks ,
the TCN outperforms baseline RNN methods (LSTMs, GRUs, and vanilla RNNs) and
 sometimes even highly specialized approaches.  We further show that the
 potential "infinite memory" advantage that RNNs have over TCNs is largely
 absent in practice: TCNs indeed exhibit longer effective history sizes than their 
 recurrent counterparts.   As a whole, we argue that it may be time to (re)consider 
 ConvNets as the default "go to" architecture for sequence modeling.

Answer:
We argue that convolutional networks should be considered the default starting point for sequence modeling tasks.