YongKang Liu, Xingle Xu, Ercong Nie, Zijing Wang, Shi Feng, Daling Wang, Qian Li, Hinrich Schuetze. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song, Hinrich Schuetze. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Peidong Wang, Zhiming Ma, Xin Dai, YongKang Liu, Shi Feng, Xiaocui Yang, Wenxing Hu, Zhihao Wang, Mingjun Pan, Li Yuan, Daling Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Zijing Wang, Xingle Xu, YongKang Liu, Yiqun Zhang, Peiqin Lin, Shi Feng, Daling Wang, Xiaocui Yang, Hinrich Schuetze. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Contrastive vision-language pre-training models such as CLIP have achieved remarkable progress in vision-language understanding. However, a modality gap still exists in the shared embedding space, which refers to the gap between image and text feature clusters, ultimately limiting the performance of downstream tasks.Most existing studies attempt to alleviate this gap by directly pulling embeddings closer within the shared space. However, they often overlook the balance between alignment and distinctiveness, leading to degraded uniformity, which can easily result in representation collapse. Our approach focuses on addressing the modality gap from modality distinctiveness. We propose CCRA (Cross-modal Complementary Representation Alignment), a lightweight post-alignment framework that explicitly models the complementarity between modalities while maintaining semantic consistency, thereby effectively alleviating the modality gap.CCRA consists of two modules: (1) a Feature Complementary Network (FCN), which dynamically extracts and fuses complementary semantic cues and learnable query vectors; (2) a Distillation Enhancement Module (DEM), which transfers similarity distributions from a frozen CLIP teacher model to maintain training stability through a distillation constraint. CCRA achieves consistent improvements over five representative baselines across two cross-modal retrieval benchmarks (COCO2017-Val and Flickr8k/30k). On COCO2017-Val, it shows an improvement of 10.06% for I→T and 6.12% for T→I, reaching the best overall performance. Both quantitative and qualitative analyses further confirm that CCRA effectively narrows the modality gap and produces more uniform feature distributions on the hypersphere.

Dexian Cai, Xiaocui Yang, YongKang Liu, Daling Wang, Shi Feng, Yifei Zhang, Soujanya Poria. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Although large language models(LLMs) show amazing capabilities, among various exciting applications discovered for LLMs fall short in other low-resource languages. Besides, most existing methods depend on large-scale dialogue corpora and thus building systems for dialogue generation in a zero-shot scenario remains a considerable challenge. To address this challenge, we propose a novel end-to-end zero-shot dialogue generation model ChatZero based on cross-lingual code-switching method. First, we construct code-switching language and pseudo-target language with placeholders. Then for cross-lingual semantic transfer, we employ unsupervised contrastive learning to minimize the semantics gap of the source language, code-switching language, and pseudo-target language that are mutually positive examples in the high dimensional semantic space. Experiments on the multilingual DailyDialog and DSTC7-AVSD datasets demonstrate that ChatZero can achieve more than 90% of the original performance under the zero-shot case compared to supervised learning, and achieve state-of-the-art performance compared with other baselines.

Full-parameter fine-tuning (FPFT) has become the go-to choice for adapting language models (LMs) to downstream tasks due to its excellent performance.As LMs grow in size, fine-tuning the full parameters of LMs requires a prohibitively large amount of GPU memory.Existing approaches utilize zeroth-order optimizer to conserve GPU memory, which potentially compromises the performance of LMs as non-zero order optimizers tend to converge more readily on most downstream tasks.We propose a novel, memory-efficient, optimizer-independent, end-to-end hierarchical fine-tuning strategy, HiFT, which only updates a subset of parameters at each training step.HiFT significantly reduces the amount of gradients and optimizer state parameters residing in GPU memory at the same time, thereby reducing GPU memory usage.Our results demonstrate that: (1) HiFT achieves comparable performance with parameter-efficient fine-tuning and standard FPFT.(2) Results on six models show that HiFT reduces the number of trainable parameters by about 89.18% on average compared to FPFT.(3) HiFT supports FPFT of 7B models for 24G GPU memory devices under mixed precision without using any memory saving techniques.(4) HiFT supports various optimizers including AdamW, AdaGrad, SGD, etc.The source code link is https://github.com/misonsky/HiFT.

We investigate response generation for multiturn dialogue in generative chatbots.Existing generative models based on RNNs (Recurrent Neural Networks) usually employ the last hidden state to summarize the history, which makes models unable to capture the subtle variability observed in different dialogues and cannot distinguish the differences between dialogues that are similar in composition.In this paper, we propose Pseudo-Variational Gated Recurrent Unit (PVGRU).The key novelty of PVGRU is a recurrent summarizing variable that aggregates the accumulated distribution variations of subsequences.We train PVGRU without relying on posterior knowledge, thus avoiding the training-inference inconsistency problem.PVGRU can perceive subtle semantic variability through summarizing variables that are optimized by two objectives we employ for training: distribution consistency and reconstruction.In addition, we build a Pseudo-Variational Hierarchical Dialogue (PVHD) model based on PVGRU.Experimental results demonstrate that PVGRU can broadly improve the diversity and relevance of responses on two benchmark datasets.

Although existing machine reading comprehension models are making rapid progress on many datasets, they are far from robust. In this article, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues, which are over-sensitivity, over-stability, and generalization. Specifically, we first use a natural language inference module to help the model understand the accurate semantic meanings of input questions to address the issues of over-sensitivity and over-stability. Then, in the machine reading comprehension module, we propose a memory-guided multi-head attention method that can further well understand the semantic meanings of input questions and passages. Third, we propose a multi-language learning mechanism to address the issue of generalization. Finally, these modules are integrated with a multi-task learning-based method. We evaluate our model on three benchmark datasets that are designed to measure models’ robustness, including DuReader (robust) and two SQuAD-related datasets. Extensive experiments show that our model can well address the mentioned three kinds of robustness issues. And it achieves much better results than the compared state-of-the-art models on all these datasets under different evaluation metrics, even under some extreme and unfair evaluations. The source code of our work is available at https://github.com/neukg/RobustMRC .

Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre-trained language generation models such as GPT-3 and T5. The research on zero-shot dialogue generation without cumbersome language models is limited due to lacking corresponding parallel dialogue corpora. In this paper, we propose a simple but effective Multilingual learning framework for Zero-shot Dialogue Generation (dubbed as MulZDG) that can effectively transfer knowledge from an English corpus with large-scale training samples to a non-English corpus with zero samples. Besides, MulZDG can be viewed as a multilingual data augmentation method to improve the performance of the resource-rich language. First, we construct multilingual code-switching dialogue datasets via translation utterances randomly selected from monolingual English datasets. Then we employ MulZDG to train a unified multilingual dialogue model based on the code-switching datasets. The MulZDG can conduct implicit semantic alignment between different languages. Experiments on DailyDialog and DSTC7 datasets demonstrate that MulZDG not only achieve competitive performance under zero-shot case compared to training with sufficient examples but also greatly improve the performance of the source language.

Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms.Although promising results have been achieved, these models often suffer from slow inference or huge number of parameters.In this paper, we propose a novel lightweight fully convolutional architecture, called DialogConv, for response selection.DialogConv is exclusively built on top of convolution to extract matching features of context and response.Dialogues are modeled in 3D views, where DialogConv performs convolution operations on embedding view, word view and utterance view to capture richer semantic information from multiple contextual views.On the four benchmark datasets, compared with state-of-the-art baselines, Di-alogConv is on average about 8.5× smaller in size, and 79.39× and 10.64× faster on CPU and GPU devices, respectively.At the same time, DialogConv achieves the competitive effectiveness of response selection.

Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore the following two kinds of understandings. First, to understand the semantic meaning of words in the input question and documents from the perspective of each other. Second, to understand the supporting cues for a correct answer from the perspective of intra-document and inter-documents. Ignoring these two kinds of important understandings would make the models overlook some important information that may be helpful for finding correct answers. To overcome this deficiency, we propose a deep understanding based model for multi-document machine reading comprehension. It has three cascaded deep understanding modules which are designed to understand the accurate semantic meaning of words, the interactions between the input question and documents, and the supporting cues for the correct answer. We evaluate our model on two large scale benchmark datasets, namely TriviaQA Web and DuReader. Extensive experiments show that our model achieves state-of-the-art results on both datasets.

We investigate response selection for multi-turn conversation in retrieval-based chatbots. Existing studies pay more attention to the matching between utterances and responses by calculating the matching score based on learned features, leading to insufficient model reasoning ability. In this paper, we propose a graph- reasoning network (GRN) to address the problem. GRN first conducts pre-training based on ALBERT using next utterance prediction and utterance order prediction tasks specifically devised for response selection. These two customized pre-training tasks can endow our model with the ability of capturing semantical and chronological dependency between utterances. We then fine-tune the model on an integrated network with sequence reasoning and graph reasoning structures. The sequence reasoning module conducts inference based on the highly summarized context vector of utterance-response pairs from the global perspective. The graph reasoning module conducts the reasoning on the utterance-level graph neural network from the local perspective. Experiments on two conversational reasoning datasets show that our model can dramatically outperform the strong baseline methods and can achieve performance which is close to human-level.

Relation classification is an important task in natural language processing fields. State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the relations of the labeled entity pairs are given. However, these methods usually suffer from the data sparsity issue greatly. On the other hand, we notice that it is very easily to obtain some concise text descriptions for almost all of the entities in a relation classification task. The text descriptions can provide helpful supplementary information for relation classification. But they are ignored by most of existing methods. In this paper, we propose DesRC, a new neural relation classification method which integrates entities’ text descriptions into deep neural networks models. We design a two-level attention mechanism to select the most useful information from the “intra-sentence” aspect and the “cross-sentence” aspect. Besides, the adversarial training method is also used to further improve the classification per-formance. Finally, we evaluate the proposed method on the SemEval 2010 dataset. Extensive experiments show that our method achieves much better experimental results than other state-of-the-art relation classification methods.