The prevalence of rapidly evolving slang, neologisms, and highly stylized expressions in informal user-generated text, particularly on Chinese social media, poses significant challenges for Machine Translation (MT) benchmarking. Specifically, we identify two primary obstacles: (1) data scarcity, as high-quality parallel data requires bilingual annotators familiar with platform-specific slang, and stylistic cues; and (2) metric limitations, where traditional evaluators like COMET often fail to capture stylistic fidelity and non-standard expressions. To bridge these gaps, we first introduce CSM-MTBench, an expert-curated benchmark covering five Chinese→foreign language directions and comprising 10,915 total instances, organized into two subsets: **Fun Posts**, featuring context-rich content with frequent slang and neologisms, and **Social Snippets**, focusing on concise, emotion- and style-driven expressions. Next, we propose tailored evaluation approaches for each subset, including slang translation success rate and style-preservation metrics combining embeddings and LLM-asa-judge evaluation. Experiments on over 20 models reveal substantial variation in how current MT systems handle semantic fidelity, slang translation, and stylistic nuances. We further look into model behaviors through prompt sensitivity analysis and qualitative case studies, highlighting the challenges of translating these informal expressions. CSM-MTBench thus serves as a rigorous testbed for advancing MT systems capable of mastering real-world Chinese social media texts.

Data mixture selection is critical for Large Language Model pretraining. Existing methods such as RegMix select a single static mixture by fitting a regression model on small-scale proxy runs. We propose RegMix-D, a simple extension of RegMix to dynamic mixing. Our key observation is that proxy runs produce not only endpoint losses, but also full loss trajectories, which can be used to further improve data mixture. By training regression model on these trajectories, we can predict optimal mixtures at multiple training stages. RegMix-D supports two deployment modes: an *offline* variant that generates a complete mixture schedule before target training, and an *online* variant that adapts the mixture during training using observed loss. Experiments on 25B tokens of the Pile dataset with a 1B parameter target model show that RegMix-D consistently improves over baselines across 13 downstream tasks, and the improvement carries over to a 7B target model on 100B tokens. RegMix-D also remains proxy-efficient: it surpasses RegMix even with only 128 proxy models (25% of RegMix’s proxy compute budget).

Multimodal sentence embedding models typically leverage image-caption pairs in addition to textual data during training. However, such pairs often contain noise, including redundant or irrelevant information on either the image or caption side. To mitigate this issue, we propose MCSEO, a method that enhances multimodal sentence embeddings by incorporating fine-grained object-phrase alignment alongside traditional image-caption alignment. Specifically, MCSEO utilizes existing detection and segmentation models to extract accurate object-phrase pairs, which are then used to optimize a proposed contrastive learning objective tailored to object-phrase correspondence. Experimental results on semantic textual similarity (STS) tasks across different backbone models demonstrate that MCSEO outperforms image-caption-only baselines on average, and that it is complementary to existing methods: combining MCSEO with KDMCSE yields further gains. A random-pairing control and a manual evaluation of the extracted pairs additionally indicate that the improvements stem from correct object-phrase alignment.

Neologism-aware machine translation aims to translate source sentences containing neologisms into target languages.This field remains underexplored compared with general machine translation (MT).In this paper, we propose an agentic framework, NeoAMT, for neologism-aware machine translation equipped with a Wiktionary-based search toolkit.Specifically, we first construct a dedicated dataset for neologism-aware machine translation and build a search toolkit grounded in Wiktionary.The dataset covers 16 languages and 75 translation directions in total, derived from approximately 10 million records of an English Wiktionary dump.The retrieval corpus of the search toolkit is also constructed from around 3 million cleaned records of the same dump.We then leverage the dataset and toolkit to train a translation agent via reinforcement learning (RL) and to evaluate the accuracy of neologism-aware machine translation.Furthermore, we propose an RL training framework featuring a novel reward design and an adaptive rollout generation strategy that exploits translation difficulty to further improve the translation quality of translation agents using our search toolkit.

Recently, many works have been attempting to adapt Large Language Models (LLMs) for sentence embedding, with most of them fine-tuning LLMs towards the contrastive objective and enabling bi-directional attention for better performance, using LoRA to address the large model scale.In this work, we suggest that this adaptation can also be simply and effectively achieved using causal attention and with even fewer trainable parameters through soft prompt tuning, as an alternative to fine-tuning with LoRA and other methods with extra post-training tasks.Our method only optimizes a few learnable tokens while keeping the rest of the model frozen.Through experiments on a diverse set of evaluation tasks, we find that simply tuning only a few tokens can achieve a competitive performance with that of fine-tuning with LoRA. The percentage of trainable parameters can be reduced to less than 0.001%. Moreover, we also demonstrate that turning causal attention to bi-directional attention with or without extra post-training tasks does not provide additional benefit when soft prompt tuning is applied, suggesting that causal attention can be naturally used in decoder-only LLMs for sentence embedding adaptation.

Word alignment plays a crucial role in various natural language processing tasks, such as serving as cross-lingual signals for sentence embedding, reducing hallucination and omission in machine translation, and facilitating the construction of training data for simultaneous speech translation.Current state-of-the-art approaches usually rely on: (1) supervised data and large-scale weakly supervised data constructed from Wikipedia and (2) multilingual Transformer encoder-based models.However, we find that the current state-of-the-art encoder-based method, BinaryAlign, suffers from the issue of insufficient labeled data, and we further improve it with self-training with a small amount of parallel data. In addition, considering the impressive performance of multilingual large language models on many natural language processing tasks, we also explore the possibility of using these decoder-based large language models as word aligners. We observe that although fine-tuning large language models with labeled data produces acceptable results, augmenting the training with pseudo-labeled data further enhances model performance. Based on the findings, we propose a semi-supervised framework to improve the large language model-based word aligners. Experimental results demonstrate that the proposed method with a small amount of parallel data outperforms the current state-of-the-art method on various word alignment datasets.

The field of cross-lingual sentence embeddings has recently experienced significant advancements, but research concerning low-resource languages has lagged due to the scarcity of parallel corpora. This paper shows that cross-lingual word representation in low-resource languages is notably under-aligned with that in high-resource languages in current models. To address this, we introduce a novel framework that explicitly aligns words between English and eight low-resource languages, utilizing off-the-shelf word alignment models. This framework incorporates three primary training objectives: aligned word prediction and word translation ranking, along with the widely used translation ranking. We evaluate our approach through experiments on the bitext retrieval task, which demonstrate substantial improvements on sentence embeddings in low-resource languages. In addition, the competitive performance of the proposed model across a broader range of tasks in high-resource languages underscores its practicality.

The problem of hallucination and omission, a long-standing problem in machine translation (MT), is more pronounced when a large language model (LLM) is used in MT because an LLM itself is susceptible to these phenomena. In this work, we mitigate the problem in an LLM-based MT model by guiding it to better word alignment. We first study the correlation between word alignment and the phenomena of hallucination and omission in MT. Then we propose to utilize word alignment as preference to optimize the LLM-based MT model. The preference data are constructed by selecting chosen and rejected translations from multiple MT tools. Subsequently, direct preference optimization is used to optimize the LLM-based model towards the preference signal. Given the absence of evaluators specifically designed for hallucination and omission in MT, we further propose selecting hard instances and utilizing GPT-4 to directly evaluate the performance of the models in mitigating these issues. We verify the rationality of these designed evaluation methods by experiments, followed by extensive results demonstrating the effectiveness of word alignment-based preference optimization to mitigate hallucination and omission. On the other hand, although it shows promise in mitigating hallucination and omission, the overall performance of MT in different language directions remains mixed, with slight increases in BLEU and decreases in COMET.