Short-video platforms have rapidly become a new generation of information retrieval systems, where users formulate queries to access desired videos. However, user queries, especially long-tail ones, often suffer from spelling errors, incomplete phrasing, and ambiguous intent, resulting in mismatches between user expectations and retrieved results. While large language models (LLMs) have shown success in long-tail query rewriting within e-commerce, they struggle on short-video platforms, where proprietary content such as short videos, live streams, micro dramas, and user social networks falls outside their training distribution. To address this challenge, we introduce CardRewriter, an LLM-based framework that incorporates domain-specific knowledge to enhance long-tail query rewriting. For each query, our method aggregates multi-source knowledge relevant to the query and summarizes it into an informative and query-relevant knowledge card. This card then guides the LLM to better capture user intent and produce more effective query rewrites. We optimize CardRewriter using a two-stage training pipeline: supervised fine-tuning followed by group relative policy optimization, with a tailored reward system balancing query relevance and retrieval effectiveness. Offline experiments show that CardRewriter substantially improves rewriting quality for queries targeting proprietary content. Online A/B testing further confirms significant gains in long-view rate (LVR) and click-through rate (CTR), along with a notable reduction in initiative query reformulation rate (IQRR). Since September 2025, CardRewriter has been deployed on Kuaishou, one of China's largest short-video platforms, serving hundreds of millions of users daily.

In parataxis languages like Chinese, word meanings are highly correlated with morphological knowledge, which can help to disambiguate word senses.However, in-depth exploration of morphological knowledge in previous word sense disambiguation (WSD) methods is still lacking due to the absence of publicly available resources.In this paper, we are motivated to enhance Chinese WSD with full morphological knowledge, including both word-formations and morphemes.We first construct the largest releasable Chinese WSD resources, including the lexico-semantic inventories MorInv and WrdInv, a Chinese WSD dataset MiCLS, and an out-of-volcabulary (OOV) test set.Then, we propose a model, MorBERT, to fully leverage this morphology-informed knowledge for Chinese WSD and achieve a SOTA F1 of 92.18% on MiCLS dataset.Finally, we demonstrated the model's robustness in low-resource settings and generalizability to OOV senses.These resources and methods may bring new insights into and solutions for various downstream tasks in both computational and humanistic fields 1 .

Morphemes serve as a strong linguistic feature to capture lexical semantics, with higher coverage than words and more natural than sememes. However, due to the lack of morpheme-informed resources and the expense of manual annotation, morpheme-enhanced methods remain largely unexplored in Computational Linguistics. To address this issue, we propose the task of Morpheme Sense Disambiguation (MSD), with two subtasks in-text and in-word, similar to Word Sense Disambiguation (WSD) and Sememe Prediction (SP), to generalize morpheme features on more tasks. We first build the MorDis resource for Chinese, including MorInv as a morpheme inventory, MorTxt and MorWrd as two types of morpheme-annotated datasets. Next, we provide two baselines in each evaluation; the best model yields a promising precision of 77.66% on in-text MSD and 88.19% on in-word MSD, indicating its comparability with WSD and superiority over SP. Finally, we demonstrate that predicted morphemes achieve comparable performance with the ground-truth ones on a downstream application of Definition Generation (DG). This validates the feasibility and applicability of our proposed tasks. The resources and workflow of MSD will provide new insights and solutions for downstream tasks, including DG as well as WSD, training pre-trained models, etc.

Previous evaluations of large language models (LLMs) focused on the perspective of various tasks or abilities. In this paper, we propose to evaluate from a linguistic viewpoint and argue that morpheme, a potential linguistic feature that captures both word-formation and lexical semantics, is another suitable component for evaluation that remains largely unexplored. In light of this, we construct MorphEval, a morpheme-informed benchmark, including three datasets following the bottom-up levels of characters, words, and sentences in Chinese, and then evaluate representative LLMs with both zero- and few-shot settings under two metrics. From this perspective, we reveal three aspects of issues LLMs nowadays encounter: dysfunctions in morphology and syntax, challenges with the long-tailed distribution of semantics, and difficulties from cultural implications. In these scenarios, even a smaller Chinese-targeted model may outperform ChatGPT, highlighting the actual challenges LLMs face and the necessity of language-specific improvements when applied to non-English languages. This new approach could also help guide model enhancements as well as get extended to other languages.

Emotion-cause pair extraction (ECPE), an emerging task in sentiment analysis, aims at extracting pairs of emotions and their corresponding causes in documents. This is a more challenging problem than emotion cause extraction (ECE), since it requires no emotion signals which are demonstrated as an important role in the ECE task. Existing work follows a two-stage pipeline which identifies emotions and causes at the first step and pairs them at the second step. However, error propagation across steps and pair combining without contextual information limits the effectiveness. Therefore, we propose a Dual-Questioning Attention Network to alleviate these limitations. Specifically, we question candidate emotions and causes to the context independently through attention networks for a contextual and semantical answer. Also, we explore how weighted loss functions in controlling error propagation between steps. Empirical results show that our method performs better than baselines in terms of multiple evaluation metrics. The source code can be obtained at https://github.com/QixuanSun/DQAN.