29 accepted papers
Knowledge Graph Embedding (KGE) aims to map entities and relationships into a continuous vector space to facilitate reasoning and downstream tasks. Although previous KGE methods based on Euclidean, complex spaces, or hyperbolic spaces have performed well, they still struggle to effectively model Z-P
Knowledge graph embedding techniques have emerged as a critical approach for addressing the issue of missing relations in knowledge graphs. However, existing methods often suffer from limitations, including high intra-group similarity, loss of semantic information, and insufficient inference capabil…
Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet either through semantic matching or distance scoring functions. However, it appear…
Document-level Event Extraction (DEE) aims to identify event information from a given document. The two challenges of this task are the event arguments scattering across differrent sentences and the multiple events within a single document. In this paper, we propose a novel Dynamic Structure Hypergr…
With the growing adoption of large language models (LLMs) in NLP tasks, concerns about their fairness have intensified. Yet, most existing fairness benchmarks rely on closed-ended evaluation formats, which diverge from real-world open-ended interactions. These formats are prone to position bias and
Representation learning in knowledge graphs (KGs) has predominantly focused on static data, yet many real-world knowledge graphs are inherently dynamic. For instance, the fact (The CEO of Apple, holds position, Steve Jobs) was valid until 2011, after which it changed, emphasizing the need to incorpo
As large language models (LLMs) are increasingly applied to various NLP tasks, their inherent biases are gradually disclosed. Therefore, measuring biases in LLMs is crucial to mitigate its ethical risks. However, most existing bias evaluation datasets are focus on English andNorth American culture,…
Recent studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding (TKGE) task. However, we found that inherent heterogeneity among factor tensors in tensor decomposition significantly hinders the tensor fusion process and further limits the…
The great challenge of handwritten mathematical expression recognition (HMER) is the complex structures of the expressions, which are directly related to the symbol spatial positions. Existing HMER methods typically employ attention mechanisms in the decoder of their models to implicitly perceive th…
The zero-shot sketch-based image retrieval task faces two key challenges: domain gap and knowledge transfer. Our innovation is recognizing that directly aligning cross-domain features weakens the discriminative ability of the model, as it overlooks the asymmetry between sketches and images. Addition…
Low-light image enhancement (LLIE) is a fundamental task in computer vision. Its goal is to extract more useful information from dark regions. Many existing methods have made excellent strides in improving image brightness and enhancing texture details. However, these approaches often lead to overex…
Entity alignment (EA) aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Euclidean space. However, KGs often contain complex local and hierarchical structures, which are hard to represent in…
Existing models for diverse generative reasoning still struggle to generate multiple unique and plausible results. Through an in-depth examination, we argue that it is critical to leverage a mixture of experts as prefixes to enhance the diversity of generated results and make task-oriented adaptatio…
The mainstream approaches view the knowledge graph-to-text (KG-to-text) generation as a sequence-to-sequence task and fine-tune the pre-trained model (PLM) to generate the target text from the linearized knowledge graph. However, the linearization of knowledge graphs and the structure of PLMs lead t…
Knowledge graph embedding (KGE) is extensively employed for link prediction by representing entities and relations as low-dimensional vectors. In real-world scenarios, knowledge graphs (KGs) usually encompass diverse domains, which poses challenges to KG representations. However, existing KGE method…
Linear Graph Convolutional Networks (GCNs) are used to classify the node in the graph data. However, we note that most existing linear GCN models perform neural network operations in Euclidean space, which do not explicitly capture the tree-like hierarchical structure exhibited in real-world dataset…
This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding models. Different from the previous translation-based models, TransERR encodes…
Multimodal emotion recognition leverages complementary information across modalities to gain performance. However, we cannot guarantee that the data of all modalities are always present in practice. In the studies to predict the missing data across modalities, the inherent difference between heterog…
Incomplete utterance rewriting (IUR) aims to restore the incomplete utterance with sufficient context information for comprehension. This paper introduces a simple yet efficient IUR method. Different from prior studies, we first employ only one-layer MLP architecture to mine latent semantic informat…
Temporal knowledge graph embedding (TKGE) models are commonly utilized to infer the missing facts and facilitate reasoning and decision-making in temporal knowledge graph based systems. However, existing methods fuse temporal information into entities, potentially leading to the evolution of entity…
Non-autoregressive transformer (NAT) based speech recognition models have gained more and more attention since they perform faster inference speed compared with autoregressive counterparts, especially when the single-step decoding is applied. However, the single-step decoding process with length pre…
Sequence-to-sequence attention-based models for grapheme-to-phoneme (G2P) conversion have gained significant interests. The attention-based encoder-decoder framework learns the mapping of input to output tokens by selectively focusing on relevant information, and has been shown well performance. How…
This paper deals with the problem of separating target speech signal from reverberant and noisy environment with dual microphones, where the target speech comes from a predefined direction range. First, we apply two differential beamformers with opposite directions to dual-channel inputs. Then, the…
Mongolian morphological segmentation is regarded as a crucial preprocessing step in many Mongolian related NLP applications and has received extensive attention. Recently, end-to-end segmentation approaches with long short-term memory networks (LSTM) have achieved excellent results. However, the inn…
While neural end-to-end text-to-speech (TTS) is superior to conventional statistical methods in many ways, the exposure bias problem in the autoregressive models remains an issue to be resolved. The exposure bias problem arises from the mismatch between the training and inference process, that resul…
Supervised speech separation methods train learning machine to cast the noisy speech to the target clean speech. Most of them use mean-square error (MSE) as loss function. However, MSE is not the perfect choice because it doesn't match the human auditory perception. Short-time objective intelligibil…
In this paper, we propose a novel Gaussian Mixture Language Model to address the issues of the traditional bag of visual words (BoVW) based model. We firstly take full advantage of image semantic information to learn a new distance metric which can achieve the minimal loss of image information, and…
Pitch is an important characteristic of speech and is useful for many applications. However, pitch determination in noisy conditions is difficult. In this paper, we propose a supervised learning algorithm to estimate pitch using a convolutional neural network (CNN). Specifically, we use a CNN for pi…
Pitch information is an important cue for speech separation. However, pitch estimation in noisy condition is also a task as challenging as speech separation. In this paper, we propose a supervised learning architecture which combines these two problems concisely. The proposed algorithm is based on d…