Title: A database-based rather than a language model-based natural language processing method
Venue: NeurIPS 2023
Accepted: no
Keywords: sentence generation

sentence understanding

relative spatial relationship

Tree-graph hybrid model.
Abstract:  Language models applied to NLP tasks take natural language as the direct modeling object. But we believe that natural language is essentially a way of encoding information, therefore, the object of study for natural language should be the information encoded in language, and the organizational and compositional structure of the information described in language. Based on this understanding, we propose a database-based natural language processing method that changes the modeling object from natural language to the information encoded in natural language. On this basis, the sentence generation task is transformed into read operations implemented on the database and some sentence encoding rules to be followed; The sentence understanding task is transformed into sentence decoding rules and a series of Boolean operations implemented on the database. Our method is closer to the information processing mechanism of the human brain and has excellent interpretability and scalability.


Peer reviews:
comments: SUMMARY:
This paper proposes a new method for natural language processing. Instead of using language corpus, authors suggest to use database. Sentence generation is a linear schematization of a database-based representation. It is indeed an interesting idea.

STRENGTHS:
Authors propose a brand new NLP approach that is more closer to the way the human brain processes information, and very likely on the way to a brand new neural process for NLP.

WEAKNESSES:
There is no experiment described in the paper. Authors shall finish experiments, then submit papers.

QUESTIONS:
Could we understand database as a base of data, which includes natural language descriptions?

LIMITATIONS:
Please read papers about mental spatial models, to see how simple spatial descriptions are generated from mental model.
For example, why most people say, San Diego is located further west of Reno? What could be the structure of the 'spatial database' in our neural mind?
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 2
DATE: 2023-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to take a novel standpoint with respect to all the neural network architectures working on natural language (NN-NL). According to the authors, these NN-NLs work on the surface of the language, disregarding that the language is encoding information. In their opinion, information should be represented as entities and relations as in databases. Consequently, the paper proposes NN architectures working on trees and working on different levels of information encoding (Fugure 5). Clearly, relations have properties such as transitivity that should be used during training and inference.\n\nSTRENGTHS:\n- The paper seems to propose a revolutionary way of thinking to neural networks\n\nWEAKNESSES:\n- The idea of information is a little weird. First, in this paper, the term information stands for structured information, and, apparently, the overall idea is to use structural information as the native form of encoding information as in databases. This is a little strange as there are not very large corpora of structured information. Indeed, having a large corpus is the key of success for these neural networks.\n\n- The paper does not mention that the overall field of NLP was devoted to take Natural Language to a structured representation. From there, tasks were solved, eventually going back to natural language for specific tasks like dialog, question answering, and so on. This structured representation is at different levels: morphology, syntax, semantics, and, sometimes, pragmatics (e.g., in the form of speech acts). Only in the last decades NLP has pushed tasks from NL to NL (e.g., natural language inference, dialog) with architectures based on Machine Learning, which may be agnostic with respect to the structured representation of language utterances.\n\n- Structured information mentioned in the paper may be correlated with the semantic representation of natural language. This is not even mentioned.\n\n- There is a large body of studies on probing transformers to see if they replicate an NLP pipeline and to understand how they encode syntax, semantics and so on\n\n- The basic idea of NN is to encode text, which may be retrieved and used by means of other similar text. This is encoding \"information.\"\n\n- The paper does not propose a dataset to work with.\n\n- The paper has no experimental section\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nNot applicable", "TITLE": "Official Review", "RECOMMENDATION": 1, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 1, "DATE": "2023-07-01"}

comments: SUMMARY:
This paper studies a database-based natural language processing method and proposes a tree-graph hybrid model based on three types of spatial relations. The model is further applied to both natural language generation and natural language understanding tasks.

STRENGTHS:
The insight of borrowing human cognition to develop a natural language model processing method is worth exploring.

WEAKNESSES:
This paper lacks experimental observations and analyses to validate the effectiveness of the proposed method or support the conclusion.

QUESTIONS:
1. Capitalization typo in the title, line 9, and line 49.
2. Adequate literature review is needed.

LIMITATIONS:
Experimental results and analyses are needed to validate the effectiveness of the proposed method.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 1
DATE: 2023-07-07

comments: SUMMARY:
This paper advocates the separation of language and knowledge. It proposes a database-NLP method. The knowledge is contained in one or more databases whose internal structures can be a tree, a graph, or a hybrid. There are associated methods to query and retrieve from the database(s) the information. Finally, some "chain" based NLP methods connect the various tasks with the databases. In this paper, the database holds spatial relationship among various locations (e.g. Duck University is a location, Tennessee is a location, "the fridge" and "Tom's room" are locations). Overall, this reviewer feels that the approach proposed in this paper is very close to known art.

STRENGTHS:
This approach has a very focused usage scenario. It can be used in a situation where "hallucination by modern language model" is not permitted.

WEAKNESSES:
Overall, this reviewer feels that the approach proposed in this paper is very close to known art.

QUESTIONS:
This reviewer does not have any questions to the authors.

LIMITATIONS:
The authors clearly presented the limitations.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 2
DATE: 2023-07-08