GenAI4ELab/papercli-papers huggingface dataset:
Bridge to Non-Barrier Communication: Gloss-Prompted Fine-Grained Cued Speech Gesture Generation with Diffusion Model
Authors: Wentao Lei; Li Liu; Jun Wang
Venue: IJCAI 2024 (main)

Abstract: Cued Speech (CS) is an advanced visual phonetic encoding system that integrates lip reading with hand codings, enabling people with hearing impairments to communicate efficiently. CS video generation aims to produce specific lip and gesture movements of CS from audio or text inputs. The main challenge is that given limited CS data, we strive to simultaneously generate fine-grained hand and finger movements, as well as lip movements, meanwhile the two kinds of movements need to be asynchronously aligned. Existing CS generation methods are fragile and prone to poor performance due to template-based statistical models and careful hand-crafted pre-processing to fit the models. Therefore, we propose a novel Gloss-prompted Diffusion-based CS Gesture generation framework (called GlossDiff). Specifically, to integrate additional linguistic rules knowledge into the model. we first introduce a bridging instruction called Gloss, which is an automatically generated descriptive text to establish a direct and more delicate semantic connection between spoken language and CS gestures. Moreover, we first suggest rhythm is an important paralinguistic feature for CS to improve the communication efficacy. Therefore, we propose a novel Audio-driven Rhythmic Module (ARM) to learn rhythm that matches audio speech. Moreover, in this work, we design, record, and publish the first Chinese CS dataset with four CS cuers. Extensive experiments demonstrate that our method quantitatively and qualitatively outperforms current state-of-the-art (SOTA) methods. We will release the code and data at glossdiff.github.io/.
Reframing Spatial Reasoning Evaluation in Language Models: A Real-World Simulation Benchmark for Qualitative Reasoning
Authors: Fangjun Li; David C. Hogg; Anthony G. Cohn
Venue: IJCAI 2024 (main)

Abstract: Spatial reasoning plays a vital role in both human cognition and machine intelligence, prompting new research into language models' (LMs) capabilities in this regard. However, existing benchmarks reveal shortcomings in evaluating qualitative spatial reasoning (QSR). These benchmarks typically present oversimplified scenarios or unclear natural language descriptions, hindering effective evaluation. We present a novel benchmark for assessing QSR in LMs, which is grounded in realistic 3D simulation data, offering a series of diverse room layouts with various objects and their spatial relationships. This approach provides a more detailed and context-rich narrative for spatial reasoning evaluation, diverging from traditional, toy-task-oriented scenarios. Our benchmark encompasses a broad spectrum of qualitative spatial relationships, including topological, directional, and distance relations. These are presented with different viewing points, varied granularities, and density of relation constraints to mimic real-world complexities. A key contribution is our logic-based consistency-checking tool, which enables the assessment of multiple plausible solutions, aligning with real-world scenarios where spatial relationships are often open to interpretation. Our benchmark evaluation of advanced LMs reveals their strengths and limitations in spatial reasoning. They face difficulties with multi-hop spatial reasoning and interpreting a mix of different view descriptions, pointing to areas for future improvement.
Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors
Authors: Guozheng Li; Peng Wang; Jiajun Liu; Yikai Guo; Ke Ji; Ziyu Shang; Zijie Xu
Venue: IJCAI 2024 (main)

Abstract: Relation extraction (RE) is an important task that aims to identify the relationships between entities in texts. While large language models (LLMs) have revealed remarkable in-context learning (ICL) capability for general zero and few-shot learning, recent studies indicate that current LLMs still struggle with zero and few-shot RE. Previous studies are mainly dedicated to design prompt formats and select good examples for improving ICL-based RE. Although both factors are vital for ICL, if one can fundamentally boost the ICL capability of LLMs in RE, the zero and few-shot RE performance via ICL would be significantly improved. To this end, we introduce Micre (Meta In-Context learning of LLMs for Relation Extraction), a new meta-training framework for zero and few-shot RE where an LLM is tuned to do ICL on a diverse collection of RE datasets (i.e., learning to learn in context for RE). Through meta-training, the model becomes more effectively to learn a new RE task in context by conditioning on a few training examples with no parameter updates or task-specific templates at inference time, enabling better zero and few-shot task generalization. We experiment Micre on various LLMs with different model scales and 12 public RE datasets, and then evaluate it on unseen RE benchmarks under zero and few-shot settings. Micre delivers comparable or superior performance compared to a range of baselines including supervised fine-tuning and typical in-context learning methods. We find that the gains are particular significant for larger model scales, and using a diverse set of the meta-training RE datasets is key to improvements. Empirically, we show that Micre can transfer the relation semantic knowledge via relation label name during inference on target RE datasets.
Empirical Analysis of Dialogue Relation Extraction with Large Language Models
Authors: Guozheng Li; Zijie Xu; Ziyu Shang; Jiajun Liu; Ke Ji; Yikai Guo
Venue: IJCAI 2024 (main)

Abstract: Dialogue relation extraction (DRE) aims to extract relations between two arguments within a dialogue, which is more challenging than standard RE due to the higher person pronoun frequency and lower information density in dialogues. However, existing DRE methods still suffer from two serious issues: (1) hard to capture long and sparse multi-turn information, and (2) struggle to extract golden relations based on partial dialogues, which motivates us to discover more effective methods that can alleviate the above issues. We notice that the rise of large language models (LLMs) has sparked considerable interest in evaluating their performance across diverse tasks. To this end, we initially investigate the capabilities of different LLMs in DRE, considering both proprietary models and open-source models. Interestingly, we discover that LLMs significantly alleviate two issues in existing DRE methods. Generally, we have following findings: (1) scaling up model size substantially boosts the overall DRE performance and achieves exceptional results, tackling the difficulty of capturing long and sparse multi-turn information; (2) LLMs encounter with much smaller performance drop from entire dialogue setting to partial dialogue setting compared to existing methods; (3) LLMs deliver competitive or superior performances under both full-shot and few-shot settings compared to current state-of-the-art; (4) LLMs show modest performances on inverse relations but much stronger improvements on general relations, and they can handle dialogues of various lengths especially for longer sequences.
Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction
Authors: Guozheng Li; Peng Wang; Wenjun Ke; Yikai Guo; Ke Ji; Ziyu Shang; Jiajun Liu; Zijie Xu
Venue: IJCAI 2024 (main)

Abstract: Relation extraction (RE) aims to identify relations between entities mentioned in texts. Although large language models (LLMs) have demonstrated impressive in-context learning (ICL) abilities in various tasks, they still suffer from poor performances compared to most supervised fine-tuned RE methods. Utilizing ICL for RE with LLMs encounters two challenges: (1) retrieving good demonstrations from training examples, and (2) enabling LLMs exhibit strong ICL abilities in RE. On the one hand, retrieving good demonstrations is a non-trivial process in RE, which easily results in low relevance regarding entities and relations. On the other hand, ICL with an LLM achieves poor performance in RE while RE is different from language modeling in nature or the LLM is not large enough. In this work, we propose a novel recall-retrieve-reason RE framework that synergizes LLMs with retrieval corpora (training examples) to enable relevant retrieving and reliable in-context reasoning. Specifically, we distill the consistently ontological knowledge from training datasets to let LLMs generate relevant entity pairs grounded by retrieval corpora as valid queries. These entity pairs are then used to retrieve relevant training examples from the retrieval corpora as demonstrations for LLMs to conduct better ICL via instruction tuning. Extensive experiments on different LLMs and RE datasets demonstrate that our method generates relevant and valid entity pairs and boosts ICL abilities of LLMs, achieving competitive or new state-of-the-art performance on sentence-level RE compared to previous supervised fine-tuning methods and ICL-based methods.
LocMoE: A Low-overhead MoE for Large Language Model Training
Authors: Jing Li; Zhijie Sun; Xuan He; Li Zeng; Yi Lin; Entong Li; Binfan Zheng; Rongqian Zhao; Xin Chen
Venue: IJCAI 2024 (main)

Abstract: The Mixtures-of-Experts (MoE) model is a widespread distributed and integrated learning method for large language models (LLM), which is favored due to its ability to sparsify and expand models efficiently. However, the performance of MoE is limited by load imbalance and high latency of All-to-All communication, along with relatively redundant computation owing to large expert capacity. Load imbalance may result from existing routing policies that consistently tend to select certain experts. The frequent inter-node communication in the All-to-All procedure also significantly prolongs the training time. To alleviate the above performance problems, we propose a novel routing strategy that combines load balance and locality by converting partial inter-node communication to that of intra-node. Notably, we elucidate that there is a minimum threshold for expert capacity, calculated through the maximal angular deviation between the gating weights of the experts and the assigned tokens. We port these modifications on the PanGu-Σ model based on the MindSpore framework with multi-level routing and conduct experiments on Ascend clusters. The experiment results demonstrate that the proposed LocMoE reduces training time per epoch by 12.68% to 22.24% compared to classical routers, such as hash router and switch router, without impacting the model accuracy.
Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching
Authors: Zhuoran Li; Chunming Hu; Junfan Chen; Zhijun Chen; Xiaohui Guo; Richong Zhang
Venue: IJCAI 2024 (main)

Abstract: Code-switching is a data augmentation scheme mixing words from multiple languages into source lingual text. It has achieved considerable generalization performance of cross-lingual transfer tasks by aligning cross-lingual contextual word representations. However, uncontrolled and over-replaced code-switching would augment dirty samples to model training. In other words, the excessive code-switching text samples will negatively hurt the models' cross-lingual transferability. To this end, we propose a Progressive Code-Switching (PCS) method to gradually generate moderately difficult code-switching examples for the model to discriminate from easy to hard. The idea is to incorporate progressively the preceding learned multilingual knowledge using easier code-switching data to guide model optimization on succeeding harder code-switching data. Specifically, we first design a difficulty measurer to measure the impact of replacing each word in a sentence based on the word relevance score. Then a code-switcher generates the code-switching data of increasing difficulty via a controllable temperature variable. In addition, a training scheduler decides when to sample harder code-switching data for model training. Experiments show our model achieves state-of-the-art results on three different zero-shot cross-lingual transfer tasks across ten languages.
Prompt-enhanced Network for Hateful Meme Classification
Authors: Junxi Liu; Yanyan Feng; Jiehai Chen; Yun Xue; Fenghuan Li
Venue: IJCAI 2024 (main)

Abstract: The dynamic expansion of social media has led to an inundation of hateful memes on media platforms, accentuating the growing need for efficient identification and removal. Acknowledging the constraints of conventional multimodal hateful meme classification, which heavily depends on external knowledge and poses the risk of including irrelevant or redundant content, we developed Pen—a prompt-enhanced network framework based on the prompt learning approach. Specifically, after constructing the sequence through the prompt method and encoding it with a language model, we performed region information global extraction on the encoded sequence for multi-view perception. By capturing global information about inference instances and demonstrations, Pen facilitates category selection by fully leveraging sequence information. This approach significantly improves model classification accuracy. Additionally, to bolster the model's reasoning capabilities in the feature space, we introduced prompt-aware contrastive learning into the framework to improve the quality of sample feature distributions. Through extensive ablation experiments on two public datasets, we evaluate the effectiveness of the Pen framework, concurrently comparing it with state-of-the-art model baselines. Our research findings highlight that Pen surpasses manual prompt methods, showcasing superior generalization and classification accuracy in hateful meme classification tasks. Our code is available at https://github.com/juszzi/Pen.
Span-based Unified Named Entity Recognition Framework via Contrastive Learning
Authors: Hongli Mao; Xian-Ling Mao; Hanlin Tang; Yu-Ming Shang; Xiaoyan Gao; Ao-Jie Ma; Heyan Huang
Venue: IJCAI 2024 (main)

Abstract: Traditional Named Entity Recognition (NER) models are typically designed for domain-specific datasets and limited to fixed predefined types, resulting in difficulty generalizing to new domains. Recently, prompt-based generative methods attempt to mitigate this constraint by training models jointly on diverse datasets and extract specified entities via prompt instructions. However, due to autoregressive structure, these methods cannot directly model entity span and suffer from slow sequential decoding. To address these issues, we propose a novel Span-based Unified NER framework via contrastive learning (SUNER), which aligns text span and entity type representations in a shared semantic space to extract entities in parallel. Specifically, we first extract mention spans without considering entity types to better generalize across datasets. Then, by leveraging the power of contrastive learning and well-designed entity marker structure, we map candidate spans and their textual type descriptions into the same vector representation space to differentiate entities across domains. Extensive experiments on both supervised and zero/few-shot settings demonstrate that proposed SUNER model achieves better performance and higher efficiency than previous state-of-the-art unified NER models.
Separate in the Speech Chain: Cross-Modal Conditional Audio-Visual Target Speech Extraction
Authors: Zhaoxi Mu; Xinyu Yang
Venue: IJCAI 2024 (main)

Abstract: The integration of visual cues has revitalized the performance of the target speech extraction task, elevating it to the forefront of the field. Nevertheless, this multi-modal learning paradigm often encounters the challenge of modality imbalance. In audio-visual target speech extraction tasks, the audio modality tends to dominate, potentially overshadowing the importance of visual guidance. To tackle this issue, we propose AVSepChain, drawing inspiration from the speech chain concept. Our approach partitions the audio-visual target speech extraction task into two stages: speech perception and speech production. In the speech perception stage, audio serves as the dominant modality, while visual information acts as the conditional modality. Conversely, in the speech production stage, the roles are reversed. This transformation of modality status aims to alleviate the problem of modality imbalance. Additionally, we introduce a contrastive semantic matching loss to ensure that the semantic information conveyed by the generated speech aligns with the semantic information conveyed by lip movements during the speech production stage. Through extensive experiments conducted on multiple benchmark datasets for audio-visual target speech extraction, we showcase the superior performance achieved by our proposed method.
Putting Back the Stops: Integrating Syntax with Neural Topic Models
Authors: Mayank Nagda; Sophie Fellenz
Venue: IJCAI 2024 (main)

Abstract: Syntax and semantics are two key concepts for language understanding. Topic models typically represent the semantics of a text corpus, while removing syntactic information during preprocessing. Without preprocessing, the generated topics become uninterpretable because the syntactic words dominate generated topics. To learn interpretable topics while keeping valuable syntactic information, we propose a novel framework that can simultaneously learn both syntactic and semantic topics from the corpus without requiring any preprocessing. A context network leverages textual dependencies to distinguish between syntactic and semantic words, while a composite VAE topic model learns two sets of topics. We demonstrate on seven datasets that our proposed method effectively captures both syntactic and semantic representations of a corpus while outperforming state-of-the-art neural topic models and statistical topic models in terms of topic quality.
ScreenAgent: A Vision Language Model-driven Computer Control Agent
Authors: Runliang Niu; Jindong Li; Shiqi Wang; Yali Fu; Xiyu Hu; Xueyuan Leng; He Kong; Yi Chang; Qi Wang
Venue: IJCAI 2024 (main)

Abstract: Large Language Models (LLM) can invoke a variety of tools and APIs to complete complex tasks. The computer, as the most powerful and universal tool, could potentially be controlled by a trained LLM agent. Powered by the computer, we can hopefully build a more generalized agent to assist humans in various daily digital works. In this paper, we construct an environment for a Vision Language Model (VLM) agent to interact with a real computer screen. Within this environment, the agent can observe screenshots and manipulate the Graphical User Interface (GUI) by outputting mouse and keyboard actions. We also design an automated control pipeline that includes planning, acting, and reflecting phases, guiding the agent to continuously interact with the environment and complete multi-step tasks. Additionally, we construct the ScreenAgent Dataset, which collects screenshots and action sequences when completing daily computer tasks. Finally, we train a model, ScreenAgent, which achieves comparable computer control capabilities to GPT-4V and demonstrated more precise UI positioning capabilities. Our attempts could inspire further research on building a generalist LLM agent. The code and more detailed information are at https://github.com/niuzaisheng/ScreenAgent.
Contrastive Representation Learning for Self-Supervised Taxonomy Completion
Authors: Yuhang Niu; Hongyuan Xu; Ciyi Liu; Yanlong Wen; Xiaojie Yuan
Venue: IJCAI 2024 (main)

Abstract: Taxonomy completion, a self-supervised task, aims to add new concepts to an existing taxonomy by attaching them to appropriate hypernym and hyponym pairs. Researchers have proposed several approaches to capture the essential relationships in taxonomy using semantic or structural information. However, they either construct training signals from a single view or simply use a random sampling strategy, making it insufficient to capture various relations in taxonomic structure and learn quality representations. To address this, we propose CoSTC, a contrastive learning framework that captures diverse relations and improves representations for taxonomy completion. It uses two contrasting views, namely intra-view and inter-view, to provide rich self-supervised signals. In intra-view contrasting, we exploit the correlations within queries and within positions by performing instance-level discrimination task. In inter-view contrasting, we use a sampling strategy that considers diversity and hardness to select representative pairs, enhancing the learning of fine-grained query-position relations. Experimental results on three datasets verify the effectiveness of our approach. Our code is available at https://github.com/nyh-a/CoSTC.
Innovative Directional Encoding in Speech Processing: Leveraging Spherical Harmonics Injection for Multi-Channel Speech Enhancement
Authors: Jiahui Pan; Pengjie Shen; Hui Zhang; Xueliang Zhang
Venue: IJCAI 2024 (main)

Abstract: Multi-channel speech enhancement leverages multiple microphones to extract target speech signals amid background noise. Effectively utilizing directional cues is key for robust enhancement. While deep learning shows promise for multi-channel speech processing, most methods operate on short-time Fourier transform (STFT) coefficients directly. We propose using spherical harmonics transform (SHT) coefficients as auxiliary inputs to models. which concisely represent spatial distributions. SHT allows signals from varying numbers of microphones to be converted into coefficients of a consistent dimension. The proposed technique enables a single model to generalize to microphone arrays with varying configurations, rather than requiring a specialized model for each array layout. We present two architectures with SHT-based auxiliary inputs: parallel and serial. Specifically, the parallel model contains two encoders - one for STFT and another for SHT. By fusing both encoders' outputs in the decoder to estimate the enhanced STFT, it effectively incorporates spatial context. For the serial approach, we first apply SHT to the signals and then take STFT of the transformed signals as network inputs. Evaluations of the TIMIT dataset under fluctuating noise and reverberation demonstrate our model outperforms established benchmarks. Remarkably, these results are attained with reduced computations and parameters. Furthermore, experiments on the MS-SNSD dataset show the proposed method can enhance the generalization ability of networks. The source code is publicly accessible at https://github.com/Pandade1997/SH_injection.
Memorizing Documents with Guidance in Large Language Models
Authors: Bumjin Park; Jaesik Choi
Venue: IJCAI 2024 (main)

Abstract: Training data plays a pivotal role in AI models. Large language models (LLMs) are trained with massive amounts of documents, and their parameters hold document-related contents. Recently, several studies identified content-specific locations in LLMs by examining the parameters. Instead of the post hoc interpretation, we propose another approach. We propose document-wise memory architecture to track document memories in training. The proposed architecture maps document representations to memory entries, which softly mask memories in the forward process of LLMs. Additionally, we propose document guidance loss, which increases the likelihood of text with document memories and reduces the likelihood of the text with the memories of other documents. Experimental results on Wikitext-103-v1 with Pythia-1B show that the proposed methods provide different memory entries for documents and high recall of document-related content in generation with trained document-wise memories.