Neural Language Modeling by Jointly Learning Syntax and Lexicon (1711.02013v2)

Abstract: We propose a neural LLM capable of unsupervised syntactic structure induction. The model leverages the structure information to form better semantic representations and better LLMing. 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 LLM, 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 LLM. In our model, the gradient can be directly back-propagated from the LLM 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 LLM tasks.

• The paper introduces PRPN, a novel language model that jointly induces syntactic structure and semantic understanding to enhance next-word prediction accuracy.
• The paper leverages a convolutional Parsing Network and a structured Reading Network to compute syntactic distances and integrate contextual memory, yielding state-of-the-art results on Penn Treebank and Text8.
• The paper validates its approach with unsupervised constituency parsing on WSJ10, underscoring its potential for language tasks in resource-scarce settings.

Evaluation of "Neural LLMing by Jointly Learning Syntax and Lexicon"

The paper "Neural LLMing by Jointly Learning Syntax and Lexicon" introduces an innovative approach to LLMing that simultaneously induces syntactic structures while improving semantic understanding. Traditional recurrent neural networks (RNNs) often overlook explicit syntactic relationships, which limits their portrayal of complex linguistic phenomena. Meanwhile, recursive networks, although useful, generally require annotated treebank data, posing challenges in scalability and data acquisition. The authors propose a novel LLM, the Parsing-Reading-Predict Network (PRPN), which surmounts these limitations by leveraging unsupervised syntactic structure induction for enhanced LLM performance.

The PRPN framework integrates three components: the Parsing Network, Reading Network, and Predict Network. The Parsing Network employs a convolutional neural network to compute syntactic distances, facilitating the identification of constituent boundaries in a sentence. The Reading Network uses a structured attention mechanism to efficiently summarize information relevant to each time step, influenced by syntactically related prior memories. Finally, the Predict Network utilizes this syntactically informed memory to predict the next word, essentially modeling next-word distributions in the context of previously established syntactic relations.

The proposed model's capability is evaluated through tasks such as word-level and character-level LLMing, alongside unsupervised constituency parsing. The empirical results are noteworthy, with the PRPN achieving state-of-the-art performance in LLMing tasks. Specifically, on the Penn Treebank and Text8 datasets for character and word-level LLMing, PRPN exhibits superior or comparable perplexity and bits-per-character scores relative to existing methods, highlighting its competency in handling both short and long-range dependencies.

An essential contribution of the paper is its approach to syntactic distance computation, which underscores the separation between successive tokens based on their syntactic relationships. This distance measure is pivotal for guiding the structured attention mechanism within the Reading Network, thus enabling the model to learn effective hierarchical abstractions mirrored in linguistic syntax.

The findings from the unsupervised constituency parsing task further validate the model's capacity to discern syntactic structures aligned with human linguistic intuition. When tested on the WSJ10 dataset, the PRPN's ability to generate meaningful syntactic hierarchies is corroborated, thus strengthening its utility in tasks necessitating syntactic understanding.

The methodological innovation of PRPN holds practical implications for NLP applications beyond LLMing. Unsupervised syntactic induction could be instrumental for languages lacking annotated resources or in situations where linguistic data is dynamically evolving, such as social media discourse. This model scaffolding potential warrants further exploration and optimization, potentially integrating with broader NLP frameworks like machine translation and language comprehension tasks.

In terms of theoretical implications, the success of the PRPN underscores the importance of syntactic awareness in neural networks. This work indicates that syntax-attachment mechanisms, previously thought unnecessary due to the opaque representations in deep networks, play a vital role in encoding complex language phenomena. Future research might consider sophistications of the PRPN model, such as exploring other architectures that might offer computational efficiencies or testing the model's robustness across divergent linguistic genres.

In summary, the "Neural LLMing by Jointly Learning Syntax and Lexicon" paper presents a noteworthy advance in the domain of neural LLMing, offering methodologies that blend syntactic parsing and language representation learning effectively and efficiently. This paper concretely illustrates the intersection of syntactic parsing with semantic LLMing, paving the way for future innovations in language processing techniques and applications.