28 accepted papers
Simultaneous speech translation (SimulST) produces translations incrementally while processing partial speech input. Although large language models (LLMs) have shown strong capabilities in offline translation tasks, applying them to SimulST poses notable challenges. Existing LLM-based SimulST appr
Existing multimodal reasoning approaches predominantly follow two paradigms: converting visual inputs into text prior to reasoning, or performing end-to-end reasoning within a unified vision–language representation space. Despite their empirical progress, both paradigms suffer from fundamental struc…
Speech translation (ST) aims to translate speech from a source language into text in the target language. Naturally, speech signals contain paralinguistic cues beyond linguistic content, which could influence or even alter the interpretation of a lexically identical sentence, thereby yielding distin
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding and generation by integrating visual and textual data. However, these models frequently exhibit object hallucination problems: generating outputs that are inconsistent with the input image. Exi
Code-switching (CS) speech translation (ST) aims to translate speech that alternates between multiple languages into a target language text, posing significant challenges due to the complexity of semantic modeling and the scarcity of CS data. Previous studies mainly rely on the models themselves to
This paper presents an efficient dual-stream early fusion method for sign language translation. Inspired by the brain’s ability to process color, shape, and motion simultaneously, the method explores complex dependencies between RGB and keypoint streams, improving speed and efficiency. A key challen…
Sign Language Translation (SLT) bridges the communication gap between deaf and hearing individuals by converting sign language videos into spoken language texts. While most SLT research has focused on bilingual translation models, the recent surge in interest has led to the exploration of Multilingu…
When the complete source sentence is provided, Large Language Models (LLMs) perform excellently in offline machine translation even with a simple prompt “Translate the following sentence from [src lang] into [tgt lang]:”. However, in many real scenarios, the source tokens arrive in a streaming manne…
Speech-to-text translation (ST) is a cross-modal task that involves converting spoken language into text in a different language. Previous research primarily focused on enhancing speech translation by facilitating knowledge transfer from machine translation, exploring various methods to bridge the g…
The widespread adoption of large language models (LLMs) has increased the need for reliable AI-text detection. While current detectors perform well on benchmark datasets, we highlight a critical vulnerability: increasing the temperature parameter during inference significantly reduces detection accu
Traditional non-simultaneous Sign Language Translation (SLT) methods, while effective for pre-recorded videos, face challenges in real-time scenarios due to inherent inference delays. The emerging field of simultaneous SLT aims to address this issue by progressively translating incrementally receive…
Sign Language Translation (SLT) aims to convert sign language videos into corresponding spoken text sequences. However, the inherent modality gap between sign language video and text hinders the development of SLT. Motivated by the linguistic consistency between gloss <sup xmlns:mml="http://www.w3.o…
Sign language translation (SLT) aims to convert continuous sign language videos into textual sentences. As a typical multi-modal task, there exists an inherent modality gap between sign language videos and spoken language text, which makes the cross-modal alignment between visual and textual modalit…
Existing neural models are demonstrated to struggle with compositional generalization (CG), i.e., the ability to systematically generalize to unseen compositions of seen components. A key reason for failure on CG is that the syntactic and semantic representations of sequences in both the uppermost l…
The primary objective of sign language translation (SLT) is to transform sign language videos into natural sentences.A crucial challenge in this field is developing signer-independent SLT systems which requires models to generalize effectively to signers not encountered during training.This challeng…
Pre-trained speech models, such as wav2vec 2.0, have significantly advanced speech-related tasks, including speech recognition and translation. However, their applicability in streaming scenarios is limited because these models are trained on complete utterances, leading to a mismatch with increment…
Sign Language Translation (SLT) is a promising technology to bridge the communication gap between the deaf and the hearing people. Recently, researchers have adopted Neural Machine Translation (NMT) methods, which usually require large-scale corpus for training, to achieve SLT. However, the publicly…
Sign language recognition (SLR) is a weakly supervised task that annotates sign videos as textual glosses. Recent studies show that insufficient training caused by the lack of large-scale available sign datasets becomes the main bottleneck for SLR. Most SLR works thereby adopt pretrained visual modu…
Most existing studies on Sign Language Translation (SLT) employ AutoRegressive Decoding Mechanism (AR-DM) to generate target sentences. However, the main disadvantage of the AR-DM is high inference latency. To address this problem, we introduce Non-AutoRegressive Decoding Mechanism (NAR-DM) into SL…
Conventional knowledge distillation(KD) approaches are commonly employed to compress neural machine translation(NMT) models. However, they only obtain one lightweight student each time. Consequently, we have to conduct KD multiple times when different students are required at the same time, which co…
In document-level relation extraction (RE), the models are required to correctly predict implicit relations in documents via relational reasoning. To this end, many graph-based methods have been proposed for this task. Despite their success, these methods still suffer from several drawbacks: 1) the…
Document-level relation extraction (RE) aims to extract relational triples from a document. One of its primary challenges is to predict implicit relations between entities, which are not explicitly expressed in the document but can usually be extracted through relational reasoning. Previous methods…
Few-shot relation extraction (FSRE) aims to train a model that can deal with new relations using only a few labeled examples. Most existing studies employ Prototypical Networks for FSRE, which usually overfits the relation classes in the training set and cannot generalize well to unseen relations. B…
Recent studies have shown that sequence-to-sequence (seq2seq) models struggle with compositional generalization (CG), i.e., the ability to systematically generalize to unseen compositions of seen components. There is mounting evidence that one of the reasons hindering CG is the representation of the…
When the images are formulated as discrete measures, computing Wasserstein-p distance between them is challenging due to the complexity of solving the corresponding Kantorovich's problem. In this paper, we propose a novel algorithm to compute the Wasserstein-p distance between discrete measures by r…
Document-level relation extraction (RE) aims to extract the relations between entities from the input document that usually containing many difficultly-predicted entity pairs whose relations can only be predicted through relational inference. Existing methods usually directly predict the relations o…
Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples. They require the model to translate both the authentic source sentence and its adversarial counterpart into t…
Research on document-level Neural Machine Translation (NMT) models has attracted increasing attention in recent years. Although the proposed works have proved that the inter-sentence information is helpful for improving the performance of the NMT models, what information should be regarded as contex…