pwc-archive/papers-with-abstracts huggingface dataset:
PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators
Authors: Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui
Date: 2024-03-22 00:00:00
Tasks: Diversity

We introduce the Proportional Payoff Allocation Game (PPA-Game) to model how agents, akin to content creators on platforms like YouTube and TikTok, compete for divisible resources and consumers' attention. Payoffs are allocated to agents based on heterogeneous weights, reflecting the diversity in content quality among creators. Our analysis reveals that although a pure Nash equilibrium (PNE) is not guaranteed in every scenario, it is commonly observed, with its absence being rare in our simulations. Beyond analyzing static payoffs, we further discuss the agents' online learning about resource payoffs by integrating a multi-player multi-armed bandit framework. We propose an online algorithm facilitating each agent's maximization of cumulative payoffs over $T$ rounds. Theoretically, we establish that the regret of any agent is bounded by $O(\log^{1 + \eta} T)$ for any $\eta > 0$. Empirical results further validate the effectiveness of our approach.
Exploring new territory: Calibration-free decoding for c-VEP BCI
Authors: J. Thielen, J. Sosulski, M. Tangermann
Date: 2024-03-22 00:00:00
Tasks: ERP

This study explores two zero-training methods aimed at enhancing the usability of brain-computer interfaces (BCIs) by eliminating the need for a calibration session. We introduce a novel method rooted in the event-related potential (ERP) domain, unsupervised mean maximization (UMM), to the fast code-modulated visual evoked potential (c-VEP) stimulus protocol. We compare UMM to the state-of-the-art c-VEP zero-training method that uses canonical correlation analysis (CCA). The comparison includes instantaneous classification and classification with cumulative learning from previously classified trials for both CCA and UMM. Our study shows the effectiveness of both methods in navigating the complexities of a c-VEP dataset, highlighting their differences and distinct strengths. This research not only provides insights into the practical implementation of calibration-free BCI methods but also paves the way for further exploration and refinement. Ultimately, the fusion of CCA and UMM holds promise for enhancing the accessibility and usability of BCI systems across various application domains and a multitude of stimulus protocols.
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation
Authors: Yundong Sun, Dongjie Zhu, Yansong Wang, Zhaoshuo Tian
Date: 2024-03-22 00:00:00
Tasks: Contrastive Learning, Graph Representation Learning, Representation Learning

Graph Neural Networks (GNNs) have emerged as the most powerful weapon for various graph tasks due to the message-passing mechanism's great local information aggregation ability. However, over-smoothing has always hindered GNNs from going deeper and capturing multi-hop neighbors. Unlike GNNs, Transformers can model global information and multi-hop interactions via multi-head self-attention and a proper Transformer structure can show more immunity to the over-smoothing problem. So, can we propose a novel framework to combine GNN and Transformer, integrating both GNN's local information aggregation and Transformer's global information modeling ability to eliminate the over-smoothing problem? To realize this, this paper proposes a collaborative learning scheme for GNN-Transformer and constructs GTC architecture. GTC leverages the GNN and Transformer branch to encode node information from different views respectively, and establishes contrastive learning tasks based on the encoded cross-view information to realize self-supervised heterogeneous graph representation. For the Transformer branch, we propose Metapath-aware Hop2Token and CG-Hetphormer, which can cooperate with GNN to attentively encode neighborhood information from different levels. As far as we know, this is the first attempt in the field of graph representation learning to utilize both GNN and Transformer to collaboratively capture different view information and conduct cross-view contrastive learning. The experiments on real datasets show that GTC exhibits superior performance compared with state-of-the-art methods. Codes can be available at https://github.com/PHD-lanyu/GTC.
Privacy-Preserving End-to-End Spoken Language Understanding
Authors: Yinggui Wang, Wei Huang, Le Yang
Date: 2024-03-22 00:00:00
Tasks: Privacy Preserving, speech-recognition, Speech Recognition, Spoken Language Understanding

Spoken language understanding (SLU), one of the key enabling technologies for human-computer interaction in IoT devices, provides an easy-to-use user interface. Human speech can contain a lot of user-sensitive information, such as gender, identity, and sensitive content. New types of security and privacy breaches have thus emerged. Users do not want to expose their personal sensitive information to malicious attacks by untrusted third parties. Thus, the SLU system needs to ensure that a potential malicious attacker cannot deduce the sensitive attributes of the users, while it should avoid greatly compromising the SLU accuracy. To address the above challenge, this paper proposes a novel SLU multi-task privacy-preserving model to prevent both the speech recognition (ASR) and identity recognition (IR) attacks. The model uses the hidden layer separation technique so that SLU information is distributed only in a specific portion of the hidden layer, and the other two types of information are removed to obtain a privacy-secure hidden layer. In order to achieve good balance between efficiency and privacy, we introduce a new mechanism of model pre-training, namely joint adversarial training, to further enhance the user privacy. Experiments over two SLU datasets show that the proposed method can reduce the accuracy of both the ASR and IR attacks close to that of a random guess, while leaving the SLU performance largely unaffected.
Sequential Decision-Making for Inline Text Autocomplete
Authors: Rohan Chitnis, Shentao Yang, Alborz Geramifard
Date: 2024-03-21 00:00:00
Tasks: Decision Making, Language Modelling, Sequential Decision Making

Autocomplete suggestions are fundamental to modern text entry systems, with applications in domains such as messaging and email composition. Typically, autocomplete suggestions are generated from a language model with a confidence threshold. However, this threshold does not directly take into account the cognitive load imposed on the user by surfacing suggestions, such as the effort to switch contexts from typing to reading the suggestion, and the time to decide whether to accept the suggestion. In this paper, we study the problem of improving inline autocomplete suggestions in text entry systems via a sequential decision-making formulation, and use reinforcement learning to learn suggestion policies through repeated interactions with a target user over time. This formulation allows us to factor cognitive load into the objective of training an autocomplete model, through a reward function based on text entry speed. We acquired theoretical and experimental evidence that, under certain objectives, the sequential decision-making formulation of the autocomplete problem provides a better suggestion policy than myopic single-step reasoning. However, aligning these objectives with real users requires further exploration. In particular, we hypothesize that the objectives under which sequential decision-making can improve autocomplete systems are not tailored solely to text entry speed, but more broadly to metrics such as user satisfaction and convenience.
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
Authors: Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang
Date: 2024-03-21 00:00:00
Tasks: Imputation

Gene regulatory network inference (GRNI) is a challenging problem, particularly owing to the presence of zeros in single-cell RNA sequencing data: some are biological zeros representing no gene expression, while some others are technical zeros arising from the sequencing procedure (aka dropouts), which may bias GRNI by distorting the joint distribution of the measured gene expressions. Existing approaches typically handle dropout error via imputation, which may introduce spurious relations as the true joint distribution is generally unidentifiable. To tackle this issue, we introduce a causal graphical model to characterize the dropout mechanism, namely, Causal Dropout Model. We provide a simple yet effective theoretical result: interestingly, the conditional independence (CI) relations in the data with dropouts, after deleting the samples with zero values (regardless if technical or not) for the conditioned variables, are asymptotically identical to the CI relations in the original data without dropouts. This particular test-wise deletion procedure, in which we perform CI tests on the samples without zeros for the conditioned variables, can be seamlessly integrated with existing structure learning approaches including constraint-based and greedy score-based methods, thus giving rise to a principled framework for GRNI in the presence of dropouts. We further show that the causal dropout model can be validated from data, and many existing statistical models to handle dropouts fit into our model as specific parametric instances. Empirical evaluation on synthetic, curated, and real-world experimental transcriptomic data comprehensively demonstrate the efficacy of our method.
Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models
Authors: Adam Karvonen
Date: 2024-03-21 00:00:00

Language models have shown unprecedented capabilities, sparking debate over the source of their performance. Is it merely the outcome of learning syntactic patterns and surface level statistics, or do they extract semantics and a world model from the text? Prior work by Li et al. investigated this by training a GPT model on synthetic, randomly generated Othello games and found that the model learned an internal representation of the board state. We extend this work into the more complex domain of chess, training on real games and investigating our model's internal representations using linear probes and contrastive activations. The model is given no a priori knowledge of the game and is solely trained on next character prediction, yet we find evidence of internal representations of board state. We validate these internal representations by using them to make interventions on the model's activations and edit its internal board state. Unlike Li et al's prior synthetic dataset approach, our analysis finds that the model also learns to estimate latent variables like player skill to better predict the next character. We derive a player skill vector and add it to the model, improving the model's win rate by up to 2.6 times.
Multiple and Gyro-Free Inertial Datasets
Authors: Zeev Yampolsky, Yair Stolero, Nitzan Pri-Hadash, Dan Solodar, Shira Massas, Itai Savin, Itzik Klein
Date: 2024-03-21 00:00:00

An inertial navigation system (INS) utilizes three orthogonal accelerometers and gyroscopes to determine platform position, velocity, and orientation. There are countless applications for INS, including robotics, autonomous platforms, and the internet of things. Recent research explores the integration of data-driven methods with INS, highlighting significant innovations, improving accuracy and efficiency. Despite the growing interest in this field and the availability of INS datasets, no datasets are available for gyro-free INS (GFINS) and multiple inertial measurement unit (MIMU) architectures. To fill this gap and to stimulate further research in this field, we designed and recorded GFINS and MIMU datasets using 54 inertial sensors grouped in nine inertial measurement units. These sensors can be used to define and evaluate different types of MIMU and GFINS architectures. The inertial sensors were arranged in three different sensor configurations and mounted on a mobile robot and a passenger car. In total, the dataset contains 35 hours of inertial data and corresponding ground truth trajectories. The data and code are freely accessible through our GitHub repository.
ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search
Authors: Zehan Li, Jianfei Zhang, Chuantao Yin, Yuanxin Ouyang, Wenge Rong
Date: 2024-03-25 00:00:00
Tasks: Code Search, Question Answering, Retrieval

Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets. Previous approaches typically rely on pretraining models using crafted bi-modal and uni-modal datasets to align text and code representations. In this paper, we introduce ProCQA, a large-scale programming question answering dataset extracted from the StackOverflow community, offering naturally structured mixed-modal QA pairs. To validate its effectiveness, we propose a modality-agnostic contrastive pre-training approach to improve the alignment of text and code representations of current code language models. Compared to previous models that primarily employ bimodal and unimodal pairs extracted from CodeSearchNet for pre-training, our model exhibits significant performance improvements across a wide range of code retrieval benchmarks.
ToXCL: A Unified Framework for Toxic Speech Detection and Explanation
Authors: Nhat M. Hoang, Xuan Long Do, Duc Anh Do, Duc Anh Vu, Luu Anh Tuan
Date: 2024-03-25 00:00:00
Tasks: Decoder, Knowledge Distillation, Text Generation

The proliferation of online toxic speech is a pertinent problem posing threats to demographic groups. While explicit toxic speech contains offensive lexical signals, implicit one consists of coded or indirect language. Therefore, it is crucial for models not only to detect implicit toxic speech but also to explain its toxicity. This draws a unique need for unified frameworks that can effectively detect and explain implicit toxic speech. Prior works mainly formulated the task of toxic speech detection and explanation as a text generation problem. Nonetheless, models trained using this strategy can be prone to suffer from the consequent error propagation problem. Moreover, our experiments reveal that the detection results of such models are much lower than those that focus only on the detection task. To bridge these gaps, we introduce ToXCL, a unified framework for the detection and explanation of implicit toxic speech. Our model consists of three modules: a (i) Target Group Generator to generate the targeted demographic group(s) of a given post; an (ii) Encoder-Decoder Model in which the encoder focuses on detecting implicit toxic speech and is boosted by a (iii) Teacher Classifier via knowledge distillation, and the decoder generates the necessary explanation. ToXCL achieves new state-of-the-art effectiveness, and outperforms baselines significantly.
Who is bragging more online? A large scale analysis of bragging in social media
Authors: Mali Jin, Daniel Preoţiuc-Pietro, A. Seza Doğruöz, Nikolaos Aletras
Date: 2024-03-25 00:00:00

Bragging is the act of uttering statements that are likely to be positively viewed by others and it is extensively employed in human communication with the aim to build a positive self-image of oneself. Social media is a natural platform for users to employ bragging in order to gain admiration, respect, attention and followers from their audiences. Yet, little is known about the scale of bragging online and its characteristics. This paper employs computational sociolinguistics methods to conduct the first large scale study of bragging behavior on Twitter (U.S.) by focusing on its overall prevalence, temporal dynamics and impact of demographic factors. Our study shows that the prevalence of bragging decreases over time within the same population of users. In addition, younger, more educated and popular users in the U.S. are more likely to brag. Finally, we conduct an extensive linguistics analysis to unveil specific bragging themes associated with different user traits.
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict
Authors: Yirong Zeng, Xiao Ding, Yi Zhao, Xiangyu Li, Jie Zhang, Chao Yao, Ting Liu, Bing Qin
Date: 2024-03-25 00:00:00
Tasks: 16k, Claim Verification, Explanation Generation, Fact Checking, Language Modeling, Language Modelling, Large Language Model

Fact-checking is the task of verifying the factuality of a given claim by examining the available evidence. High-quality evidence plays a vital role in enhancing fact-checking systems and facilitating the generation of explanations that are understandable to humans. However, the provision of both sufficient and relevant evidence for explainable fact-checking systems poses a challenge. To tackle this challenge, we propose a method based on a Large Language Model to automatically retrieve and summarize evidence from the Web. Furthermore, we construct RU22Fact, a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022 of 16K samples, each containing real-world claims, optimized evidence, and referenced explanation. To establish a baseline for our dataset, we also develop an end-to-end explainable fact-checking system to verify claims and generate explanations. Experimental results demonstrate the prospect of optimized evidence in increasing fact-checking performance and also indicate the possibility of further progress in the end-to-end claim verification and explanation generation tasks.
Grammatical vs Spelling Error Correction: An Investigation into the Responsiveness of Transformer-based Language Models using BART and MarianMT
Authors: Rohit Raju, Peeta Basa Pati, SA Gandheesh, Gayatri Sanjana Sannala, Suriya KS
Date: 2024-03-25 00:00:00
Tasks: Optical Character Recognition (OCR), speech-recognition, Speech Recognition, Text Generation, Transfer Learning

Text continues to remain a relevant form of representation for information. Text documents are created either in digital native platforms or through the conversion of other media files such as images and speech. While the digital native text is invariably obtained through physical or virtual keyboards, technologies such as OCR and speech recognition are utilized to transform the images and speech signals into text content. All these variety of mechanisms of text generation also introduce errors into the captured text. This project aims at analyzing different kinds of error that occurs in text documents. The work employs two of the advanced deep neural network-based language models, namely, BART and MarianMT, to rectify the anomalies present in the text. Transfer learning of these models with available dataset is performed to finetune their capacity for error correction. A comparative study is conducted to investigate the effectiveness of these models in handling each of the defined error categories. It is observed that while both models can bring down the erroneous sentences by 20+%, BART can handle spelling errors far better (24.6%) than grammatical errors (8.8%).
Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts
Authors: Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera
Date: 2024-03-25 00:00:00
Tasks: Decision Making, Diversity, Sentence, Sentence Embeddings

Tasks such as semantic search and clustering on crisis-related social media texts enhance our comprehension of crisis discourse, aiding decision-making and targeted interventions. Pre-trained language models have advanced performance in crisis informatics, but their contextual embeddings lack semantic meaningfulness. Although the CrisisTransformers family includes a sentence encoder to address the semanticity issue, it remains monolingual, processing only English texts. Furthermore, employing separate models for different languages leads to embeddings in distinct vector spaces, introducing challenges when comparing semantic similarities between multi-lingual texts. Therefore, we propose multi-lingual sentence encoders (CT-XLMR-SE and CT-mBERT-SE) that embed crisis-related social media texts for over 50 languages, such that texts with similar meanings are in close proximity within the same vector space, irrespective of language diversity. Results in sentence encoding and sentence matching tasks are promising, suggesting these models could serve as robust baselines when embedding multi-lingual crisis-related social media texts. The models are publicly available at: https://huggingface.co/crisistransformers.
Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units
Authors: Biswesh Mohapatra, Seemab Hassan, Laurent Romary, Justine Cassell
Date: 2024-03-25 00:00:00

Successful conversations often rest on common understanding, where all parties are on the same page about the information being shared. This process, known as conversational grounding, is crucial for building trustworthy dialog systems that can accurately keep track of and recall the shared information. The proficiencies of an agent in grounding the conveyed information significantly contribute to building a reliable dialog system. Despite recent advancements in dialog systems, there exists a noticeable deficit in their grounding capabilities. Traum provided a framework for conversational grounding introducing Grounding Acts and Grounding Units, but substantial progress, especially in the realm of Large Language Models, remains lacking. To bridge this gap, we present the annotation of two dialog corpora employing Grounding Acts, Grounding Units, and a measure of their degree of grounding. We discuss our key findings during the annotation and also provide a baseline model to test the performance of current Language Models in categorizing the grounding acts of the dialogs. Our work aims to provide a useful resource for further research in making conversations with machines better understood and more reliable in natural day-to-day collaborative dialogs.
TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniques
Authors: Ashok Urlana, Aditya Saibewar, Bala Mallikarjunarao Garlapati, Charaka Vinayak Kumar, Ajeet Kumar Singh, Srinivasa Rao Chalamala
Date: 2024-03-25 00:00:00
Tasks: Misinformation, Text Detection

The Large Language Models (LLMs) exhibit remarkable ability to generate fluent content across a wide spectrum of user queries. However, this capability has raised concerns regarding misinformation and personal information leakage. In this paper, we present our methods for the SemEval2024 Task8, aiming to detect machine-generated text across various domains in both mono-lingual and multi-lingual contexts. Our study comprehensively analyzes various methods to detect machine-generated text, including statistical, neural, and pre-trained model approaches. We also detail our experimental setup and perform a in-depth error analysis to evaluate the effectiveness of these methods. Our methods obtain an accuracy of 86.9\% on the test set of subtask-A mono and 83.7\% for subtask-B. Furthermore, we also highlight the challenges and essential factors for consideration in future studies.