Text classification is a critical and foundational task in Tibetan natural language processing, it plays a crucial role in various applications, such as sentiment analysis and information extraction. However, the limited availability of annotated data poses a significant challenge to Tibetan natural language processing. This paper proposes a prompt learning-based method for low-resource Tibetan text classification to overcome this challenge. This method utilizes pre-trained language models to learn text representation and generation capabilities on a large-scale unsupervised Tibetan corpus, enabling few-shot Tibetan text classification. Experimental results demonstrate that the proposed method significantly improves the performance of Tibetan text classification in low-resource scenarios. This work provides a new research idea and method for low-resource language processing, such as Tibetan natural language processing. Hopefully, it will inspire subsequent work on low-resource language processing.

The research of Tibetan dependency analysis is mainly limited to two challenges: lack of a dataset and reliance on expert knowledge. To resolve the preceding challenges, we first introduce a new Tibetan dependency analysis dataset, and then propose a neural-based framework that resolves the reliance on the expert knowledge issue by automatically extracting feature vectors of words and predicts their head words and type of dependency arcs. Specifically, we convert the words in the sentence into distributional vectors and employ a sequence to vector network to extract feature words. Furthermore, we introduce a head classifier and type classifier to predict the head word and type of dependency arc, respectively. Experiments demonstrate that our model achieves promising performance on the Tibetan dependency analysis task.

Natural Language Sentence Matching (NLSM) serves as the core of many natural language processing tasks.1) Most previous work develops a single specific neural model for NLSM tasks.2) There is no previous work considering adversarial attack to improve the performance of NLSM tasks.3) Adversarial attack is usually used to generate adversarial samples that can fool neural models.In this paper, we first find a phenomenon that different categories of samples have different vulnerabilities.Vulnerability is the difficulty degree in changing the label of a sample.Considering the phenomenon, we propose a general two-stage training framework to enhance neural models with Vulnerability via Adversarial Attack (VAA).We design criteria to measure the vulnerability which is obtained by adversarial attack.VAA framework can be adapted to various neural models by incorporating the vulnerability.In addition, we prove a theorem and four corollaries to explain the factors influencing vulnerability effectiveness.Experimental results show that VAA significantly improves the performance of neural models on NLSM datasets.The results are also consistent with the theorem and corollaries.The code is released on https://github.com/rzhangpku/VAA.

Word composition is a promising technique for representation learning of large linguistic units (e.g., phrases, sentences and documents). However, most of the current composition models do not take the ambiguity of words and the context outside of a linguistic unit into consideration for learning representations, and consequently suffer from the inaccurate representation of semantics. To address this issue, we propose a model-free context-aware word composition model, which employs the latent semantic information as global context for learning representations. The proposed model attempts to resolve the word sense disambiguation and word composition in a unified framework. Extensive evaluation shows consistent improvements over various strong word representation/composition models at different granularities (including word, phrase and sentence), demonstrating the effectiveness of our proposed method.

Semantic parsers critically rely on accurate and high-coverage lexicons. However, traditional semantic parsers usually utilize annotated logical forms to learn the lexicon, which often suffer from the lexicon coverage problem. In this paper, we propose a graph-based semi-supervised learning framework that makes use of large text corpora and lexical resources. This framework first constructs a graph with a phrase similarity model learned by utilizing many text corpora and lexical resources. Next, graph propagation algorithm identifies the label distribution of unlabeled phrases from labeled ones. We evaluate our approach on two benchmarks: Webquestions and Free917. The results show that, in both datasets, our method achieves substantial improvement when comparing to the base system that does not utilize the learned lexicon, and gains competitive results when comparing to state-of-the-art systems.

Bo An, Bo Chen, Xianpei Han, Le Sun. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

A major challenge of semantic parsing is the vocabulary mismatch problem between natural language and target ontology.In this paper, we propose a sentence rewriting based semantic parsing method, which can effectively resolve the mismatch problem by rewriting a sentence into a new form which has the same structure with its target logical form.Specifically, we propose two sentence-rewriting methods for two common types of mismatch: a dictionary-based method for 1-N mismatch and a template-based method for N-1 mismatch.We evaluate our sentence rewriting based semantic parser on the benchmark semantic parsing dataset -WEBQUESTIONS.Experimental results show that our system outperforms the base system with a 3.4% gain in F1, and generates logical forms more accurately and parses sentences more robustly.How many people live in chembakolli?How many people is in chembakolli?How many people live in chembakolli india?How many people live there chembakolli?How many people live there in chembakolli?What is the population of Chembakolli india?What currency is used on St Lucia?What is st lucia money?What is the money used in st lucia?What kind of money did st lucia have?What money do st Lucia use?Which money is used in St Lucia?