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Title: Neural Language Modeling by Jointly Learning Syntax and Lexicon

Paper content:
We propose a neural language model capable of unsupervised syntactic structure induction.
The model leverages the structure information to form better semantic representations and better language modeling.
Standard recurrent neural networks are limited by their structure and fail to efficiently use syntactic information.
On the other hand, tree-structured recursive networks usually require additional structural supervision at the cost of human expert annotation.
In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model.
In our model, the gradient can be directly back-propagated from the language model loss into the neural parsing network.
Experiments show that the proposed model can discover the underlying syntactic structure and achieve state-of-the-art performance on word/character-level language model tasks.
Linguistic theories generally regard natural language as consisting of two part: a lexicon, the complete set of all possible words in a language; and a syntax, the set of rules, principles, and processes that govern the structure of sentences BID46 .
To generate a proper sentence, tokens are put together with a specific syntactic structure.
Understanding a sentence also requires lexical information to provide meanings, and syntactical knowledge to correctly combine meanings.
Current neural language models can provide meaningful word represent BID0 BID41 .
However, standard recurrent neural networks only implicitly model syntax, thus fail to efficiently use structure information BID53 .Developing
a deep neural network that can leverage syntactic knowledge to form a better semantic representation has received a great deal of attention in recent years BID50 BID53 BID11 . Integrating
syntactic structure into a language model is important for different reasons: 1) to obtain
a hierarchical representation with increasing levels of abstraction, which is a key feature of deep neural networks and of the human brain BID1 BID31 BID47 ; 2) to capture complex linguistic phenomena, like long-term dependency problem BID53 and the compositional effects BID50 ; 3) to provide shortcut for gradient back-propagation BID11 .A syntactic parser
is the most common source for structure information. Supervised parsers
can achieve very high performance on well constructed sentences. Hence, parsers can
provide accurate information about how to compose word semantics into sentence semantics BID50 , or how to generate the next word given previous words BID56 . However, only major
languages have treebank data for training parsers, and it request expensive human expert annotation. People also tend to
break language rules in many circumstances (such as writing a tweet). These defects limit
the generalization capability of supervised parsers.Unsupervised syntactic structure induction has been among the longstanding challenges of computational linguistic BID23 BID25 BID2 . Researchers are interested
in this problem for a variety of reasons: to be able to parse languages for which no annotated treebanks exist BID35 ; to create a dependency structure to better suit a particular NLP application BID56 ; to empirically argue for or against the poverty of the stimulus BID12 BID10 ; and to examine cognitive issues in language learning BID51 .In this paper, we propose a
novel neural language model: Parsing-Reading-Predict Networks (PRPN), which can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to form a better language model. With our model, we assume that
language can be naturally represented as a tree-structured graph. The model is composed of three
parts:1. A differentiable neural Parsing
Network uses a convolutional neural network to compute the syntactic distance, which represents the syntactic relationships between all successive pairs of words in a sentence, and then makes soft constituent decisions based on the syntactic distance.2. A Reading Network that recurrently
computes an adaptive memory representation to summarize information relevant to the current time step, based on all previous memories that are syntactically and directly related to the current token.3. A Predict Network that predicts the
next token based on all memories that are syntactically and directly related to the next token.We evaluate our model on three tasks: word-level language modeling, character-level language modeling, and unsupervised constituency parsing. The proposed model achieves (or is
close to) the state-of-the-art on both word-level and character-level language modeling. The model's unsupervised parsing outperforms
some strong baseline models, demonstrating that the structure found by our model is similar to the intrinsic structure provided by human experts.
In this paper, we propose a novel neural language model that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model.
We introduce a new neural parsing network: Parsing-Reading-Predict Network, that can make differentiable parsing decisions.
We use a new structured attention mechanism to control skip connections in a recurrent neural network.
Hence induced syntactic structure information can be used to improve the model's performance.
Via this mechanism, the gradient can be directly backpropagated from the language model loss function into the neural Parsing Network.
The proposed model achieve (or is close to) the state-of-the-art on both word/character-level language modeling tasks.
Experiment also shows that the inferred syntactic structure highly correlated to human expert annotation.

assistant: text: In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model.