A long-standing objective in the fields of natural Secondly, while Stengel-Eskin et al. language understanding and computational semantics (2020) have tried to utilize the external tool Pred-is to create a structured graph of linguistic Patt (Zhang et al., 2017), which contains the relationship meaning. Various efforts have been made to between syntax and semantic information, encode semantic relations and attributes into a semantic they do not achieve any improvements. In graph--e.g., Abstract Meaning Representation contrast, we propose a data augmentation method (AMR; Banarescu et al., 2013), Universal that effectively exploits the capabilities of Pred-Conceptual Cognitive Annotation (UCCA; Abend Patt, leading to significant performance gains in and Rappoport, 2013), and Semantic Dependency relation parsing. Moreover, we have explored various Parsing formalisms (SDP; Oepen et al., 2014, approaches for these enhancements, providing 2016).

Punctuation restoration is an important task in automatic speech recognition (ASR) which aim to restore the syntactic structure of generated ASR texts to improve readability. While punctuated texts are abundant from written documents, the discrepancy between written punctuated texts and ASR texts limits the usability of written texts in training punctuation restoration systems for ASR texts. This paper proposes a reinforcement learning method to exploit in-topic written texts and recent advances in large pre-trained generative language models to bridge this gap. The experiments show that our method achieves state-of-the-art performance on the ASR test set on two benchmark datasets for punctuation restoration.

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken.

For a complicated algorithm, its implementation by a human programmer usually starts with outlining a rough control flow followed by iterative enrichments, eventually yielding carefully generated syntactic structures and variables in a hierarchy. However, state-of-the-art large language models generate codes in a single pass, without intermediate warm-ups to reflect the structured thought process of "outline-then-detail". Inspired by the recent success of chain-of-thought prompting, we propose ChainCoder, a program synthesis language model that generates Python code progressively, i.e. from coarse to fine in multiple passes. We first decompose source code into layout frame components and accessory components via abstract syntax tree parsing to construct a hierarchical representation. We then reform our prediction target into a multi-pass objective, each pass generates a subsequence, which is concatenated in the hierarchy. Finally, a tailored transformer architecture is leveraged to jointly encode the natural language descriptions and syntactically aligned I/O data samples. Extensive evaluations show that ChainCoder outperforms state-of-the-arts, demonstrating that our progressive generation eases the reasoning procedure and guides the language model to generate higher-quality solutions. Our codes are available at: https://github.com/VITA-Group/ChainCoder.

Code generation focuses on the automatic conversion of natural language (NL) utterances into code snippets. The sequence-to-tree (Seq2Tree) approaches are proposed for code generation, with the guarantee of the grammatical correctness of the generated code, which generate the subsequent Abstract Syntax Tree (AST) node relying on antecedent predictions of AST nodes. Existing Seq2Tree methods tend to treat both antecedent predictions and subsequent predictions equally. However, under the AST constraints, it is difficult for Seq2Tree models to produce the correct subsequent prediction based on incorrect antecedent predictions. Thus, antecedent predictions ought to receive more attention than subsequent predictions. To this end, in this paper, we propose an effective method, named Antecedent Prioritized (AP) Loss, that helps the model attach importance to antecedent predictions by exploiting the position information of the generated AST nodes. We design an AST-to-Vector (AST2Vec) method, that maps AST node positions to two-dimensional vectors, to model the position information of AST nodes. To evaluate the effectiveness of our proposed loss, we implement and train an Antecedent Prioritized Tree-based code generation model called APT. With better antecedent predictions and accompanying subsequent predictions, APT significantly improves the performance. We conduct extensive experiments on four benchmark datasets, and the experimental results demonstrate the superiority and generality of our proposed method.

Hierarchies are the hidden backbones of complex systems and their analysis allows for a deeper understanding of their structure and how they evolve. We consider languages also to be complex adaptive systems with several intricate networks that capture their structure and function. Hence, we decided to analyze the hierarchical organization of historical syntactic networks to understand how syntax evolves over time. We created these networks from a corpus of German texts from the 11th to 17th centuries, focusing on the hierarchical levels of these networks. diachronically and to map them to specific communicative needs of speakers. We developed a framework to empirically track the emergence of syntactic structures diachronically, enabling us to map the communicative needs of speakers with these structures. We named these syntactic structures "syntactic communicative hierarchies." We showed that the communicative needs of speakers are the organizational force of syntax. Thus, we argue that the emergence of syntactic communicative hierarchies plays a crucial role in shaping syntax over time. This may indicate that languages evolve not only to increase the efficiency of transferring information, but also to increase our capacity, as a species, to communicate our needs with more and more sophisticated abstractions.

Domain adaptation is an important and widely studied problem in natural language processing. A large body of literature tries to solve this problem by adapting models trained on the source domain to the target domain. In this paper, we instead solve this problem from a dataset perspective. We modify the source domain dataset with simple lexical transformations to reduce the domain shift between the source dataset distribution and the target dataset distribution. We find that models trained on the transformed source domain dataset performs significantly better than zero-shot models. Using our proposed transformations to convert standard English to tweets, we reach an unsupervised part-of-speech (POS) tagging accuracy of 92.14% (from 81.54% zero shot accuracy), which is only slightly below the supervised performance of 94.45%. We also use our proposed transformations to synthetically generate tweets and augment the Twitter dataset to achieve state-of-the-art performance for POS tagging.

We present a generic framework for data augmentation via dependency subtree swapping that is applicable to machine translation. We extract corresponding subtrees from the dependency parse trees of the source and target sentences and swap these across bisentences to create augmented samples. We perform thorough filtering based on graphbased similarities of the dependency trees and additional heuristics to ensure that extracted subtrees correspond to the same meaning. We conduct resource-constrained experiments on 4 language pairs in both directions using the IWSLT text translation datasets and the Hunglish2 corpus. The results demonstrate consistent improvements in BLEU score over our baseline models in 3 out of 4 language pairs. Our code is available on GitHub.

In the realm of software applications in the transportation industry, Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their ease of use and various other benefits. With the ceaseless progress in computer performance and the rapid development of large-scale models, the possibility of programming using natural language in specified applications - referred to as Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits greater flexibility and freedom, which, in turn, leads to an increase in computational complexity for parsing and a decrease in processing performance. To tackle this issue, our paper advances a design for an intermediate representation (IR) that caters to ASNL and can uniformly process transportation data into graph data format, improving data processing performance. Experimental comparisons reveal that in standard data query operations, our proposed IR design can achieve a speed improvement of over forty times compared to direct usage of standard XML format data.

This paper addresses the problem of improving POS tagging of transcripts of speech from clinical populations. In contrast to prior work on parsing and POS tagging of transcribed speech, we do not make use of an in domain treebank for training. Instead, we train on an out of domain treebank of newswire using data augmentation techniques to make these structures resemble natural, spontaneous speech. We trained a parser with and without the augmented data and tested its performance using manually validated POS tags in clinical speech produced by patients with various types of neurodegenerative conditions.