This paper introduces the Text-to-TrajVis task, which aims to transform natural language questions into trajectory data visualizations, facilitating the development of natural language interfaces for trajectory visualization systems. As this is a novel task, there is currently no relevant dataset available in the community. To address this gap, we first devised a new visualization language called Trajectory Visualization Language (TVL) to facilitate querying trajectory data and generating visualizations. Building on this foundation, we further proposed a dataset construction method that integrates Large Language Models (LLMs) with human efforts to create high-quality data. Specifically, we devised a four-stage pipeline that begins with candidate extraction, proceeds through seed TVL generation and tree-based expansion, and concludes with LLM-driven question creation followed by human validation. This process results in the creation of the first large-scale Text-to-TrajVis dataset, named TrajVL, which contains 9,608 (question, TVL) pairs. We propose a framework called TRCAT for progressively converting natural language questions into TVLs. The framework incorporates TVL-RAG Chain Module and Area-Time Standardization Module, significantly enhancing the accuracy of LLMs in TVL generation. Based on the TrajVL dataset, we conduct a comprehensive evaluation of TRCAT’s performance across several mainstream LLMs (e.g., GPT, Qwen, LLaMA, and Gemma). Furthermore, we established a benchmarking system for this task, providing a foundation for future research in structured trajectory language generation.

Streaming Automatic Speech Recognition (ASR), also known as online or real-time ASR, transcribes speech incrementally while audio is being spoken and is critical for many industry applications. Despite the strong performance of modern offline ASR models, streaming deployment remains challenging because limited future context can cause early recognition errors to propagate across later chunks. We define the task of streaming ASR error correction and propose StreamCorrect, a modular framework that fine-tunes a lightweight error corrector from self-generated streaming ASR errors and integrates it with existing streaming ASR systems. To enable training without human annotation, StreamCorrect introduces a timestamp-free boundary recovery procedure based on Prefix-to-Reference Alignment for generating chunk-level correction data. Across multiple languages and ASR backbones, StreamCorrect achieves up to 39% relative character error rate (CER) reduction with minimal latency overhead. Error propagation analysis further shows increasing benefits over time, with up to 7.2% absolute CER reduction in later audio chunks. StreamCorrect bridges offline ASR models and streaming deployment through a lightweight correction plug-in while preserving the pretrained ASR component without additional tuning or distillation.

Automatic Speech Recognition (ASR) is a fundamental and important task in the field of speech and natural language processing. It is an inherent building block in many applications such as voice assistant, speech translation, etc. Despite the advancement of ASR technologies in recent years, it is still inevitable for modern ASR systems to have a substantial number of erroneous recognition due to environmental noise, ambiguity, etc. Therefore, the error correction in ASR is crucial. Motivated by this, this paper studies ASR error correction in the Chinese language, which is one of the most popular languages and enjoys a large number of users in the world. We first create a benchmark dataset named ASR-EC that contains a wide spectrum of ASR errors generated by industry-grade ASR systems. To the best of our knowledge, it is the first Chinese ASR error correction benchmark. Then, inspired by the recent advances in large language models (LLMs), we investigate how to harness the power of LLMs to correct ASR errors. We apply LLMs to ASR error correction in three paradigms. The first paradigm is prompting, which is further categorized as zero-shot, few-shot, and multi-step. The second paradigm is finetuning, which finetunes LLMs with ASR error correction data. The third paradigm is multi-modal augmentation, which collectively utilizes the audio and ASR transcripts for error correction. Extensive experiments reveal that prompting is not effective for ASR error correction. Finetuning is effective only for a portion of LLMs. Multi-modal augmentation is the most effective method for error correction and achieves state-of-the-art performance.

Data visualization has emerged as an effective tool for getting insights from massive datasets. Due to the hardness of manipulating the programming languages of data visualization, automatic data visualization generation from natural languages (Text-to-Vis) is becoming increasingly popular. Despite the plethora of research effort on the English Text-to-Vis, studies have yet to be conducted on data visualization generation from questions in Chinese. Motivated by this, we propose a Chinese Text-to-Vis dataset in the paper and demonstrate our first attempt to tackle this problem. Our model integrates multilingual BERT as the encoder, boosts the cross-lingual ability, and infuses the n-gram information into our word representation learning. Our experimental results show that our dataset is challenging and deserves further research.

Transformer has been demonstrated effective in Neural Machine Translation (NMT). However, it is memory-consuming and time-consuming in edge devices, resulting in some difficulties for real-time feedback. To compress and accelerate Transformer, we propose a Hybrid Tensor-Train (HTT) decomposition, which retains full rank and meanwhile reduces operations and parameters. A Transformer using HTT, named Hypoformer, consistently and notably outperforms the recent light-weight SOTA methods on three standard translation tasks under different parameter and speed scales. In extreme low resource scenarios, Hypoformer has 7.1 points absolute improvement in BLEU and 1.27 X speedup than vanilla Transformer on IWSLT’14 De-En task.

Recently, a lot of research has been carried out to improve the efficiency of Transformer. Among them, the sparse pattern-based method is an important branch of efficient Transformers. However, some existing sparse methods usually use fixed patterns to select words, without considering similarities between words. Other sparse methods use clustering patterns to select words, but the clustering process is separate from the training process of the target task, which causes a decrease in effectiveness. To address these limitations, we design a neural clustering method, which can be seamlessly integrated into the Self-Attention Mechanism in Transformer. The clustering task and the target task are jointly trained and optimized to benefit each other, leading to significant effectiveness improvement. In addition, our method groups the words with strong dependencies into the same cluster and performs the attention mechanism for each cluster independently, which improves the efficiency. We verified our method on machine translation, text classification, natural language inference, and text matching tasks. Experimental results show that our method outperforms two typical sparse attention methods, Reformer and Routing Transformer while having a comparable or even better time and memory efficiency.