To address the problem, CLEVER leverages vision-language pre-training models for deep understanding of each image in the bag, and selects informative instances from the bag to summarize commonsense entity relations via a novel contrastive attention mechanism. Code and data are available at this https URL. Our method sets the state-of-the-art on 9 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites. The current best public system (Codex-002) achieves 43.3% accuracy, leaving ample room for improvement. In this work, we propose to leverage monolingual data to improve SiMT, which trains a SiMT student on the combination of bilingual data and external monolingual data distilled by Seq-KD. Instead, it is enough to write e.g. Fine-tuning the entire CLIP model can be resource-intensive and unstable. In this paper, we present ExtremeBERT, a toolkit for accelerating and customizing BERT pretraining. In this paper, we conduct the first study on the vulnerability of the continuous prompt learning algorithm to backdoor attacks. Experiments show that our framework achieves new SOTA results on three factual inconsistency detection tasks. Such methods usually model role classification as naive multi-class classification and treat arguments individually, which neglects label semantics and interactions between arguments and thus hindering performance and generalization of models. We design LEPISZCZE with flexibility in mind. Code is available at this https URL. Specifically, we design a learn-to-connect approach that adopts a dynamic multi-hop structure instead of a deterministic structure, and combine it with a DGCN module to automatically learn the connections between posts. Finally, a Mixture of Experts (MoE) module combines the predictions from the two models to make the final decision. Perfect isolation of run applications, without the need to use dedicated virtual environments. As a more realistic setup, we introduce the Scientific and Creative Analogy dataset (SCAN), a novel analogy dataset containing systematic mappings of multiple attributes and relational structures across dissimilar domains. This paper addresses this gap by conducting a systematic evaluation of different similarity-based and zero-shot approaches for text classification of unseen classes. With its help, you will be able to quickly and efficiently take a peek at application's structure and code. LSB Steganography is an image steganography technique in which messages are hidden inside an image by replacing each pixels least significant bit with the bits of the message to be hidden. Are you sure you want to create this branch? The pictures look like normal images, so people will not suspect there is hidden data in them. The code for PromptInject is available at this https URL. Compared with implicit error detection with CTC loss, SoftCorrect provides explicit signal about which words are incorrect and thus does not need to duplicate every token but only incorrect tokens; compared with explicit error detection, SoftCorrect does not detect specific deletion/substitution/insertion errors but just leaves it to CTC loss. However, there is still a significant performance gap between NMT and SiMT. Previous works usually control the number of words or characters generated by the machine translation model to be similar to the source sentence, without considering the isochronicity of speech as the speech duration of words/characters in different languages varies. The prompt-based learning paradigm has gained much research attention recently. Given a possibly false claim sentence, how can we automatically correct it with minimal editing? In this report, we focus on the technical designs of various system components. This paper integrates a classic mel-cepstral synthesis filter into a modern neural speech synthesis system towards end-to-end controllable speech synthesis. We find that 25% of questions contain false presuppositions, and provide annotations for these presuppositions and their corrections. We show that it measures the distance between pose sequences more accurately than existing measurements and use it to assess the quality of our generated pose sequences. If no information was hidden, you would obtain this. Prompt Tuning, conditioning on task-specific learned prompt vectors, has emerged as a data-efficient and parameter-efficient method for adapting large pretrained vision-language models to multiple downstream tasks. Currently, many research works propose to only fine-tune a small portion of the parameters while keeping most of the parameters shared across different tasks. We further investigate the effects on two new real-world short text datasets in an effort to address the issue of becoming overly dependent on benchmark datasets with a limited number of characteristics. Previous researches tend to divide FSRL into argument identification and role classification. It competes with HEIC, which uses the same container format built upon ISOBMFF, but HEVC for compression. It creates a sandbox-like isolated operating environment in which applications can be run or installed without permanently modifying the local or mapped drive. Characteristic feature of Windows applications is the fact all resources like icons, images, forms, localized texts, as well as other information, can be saved in PE file structure, within a special area called resources. Extensive experiments on three mainstream self-supervised models demonstrate the toolkit's effectiveness and achieve state-of-the-art UASR performance on TIMIT and LibriSpeech datasets. Under both few-shot and zero-shot settings, PoT can show an average performance gain over CoT by around 12\% across all the evaluated datasets. As a pioneering exploration that expands event detection to the scenarios involving informal and heterogeneous texts, we propose a new large-scale Chinese event detection dataset based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service. Online data streams make training machine learning models hard because of distribution shift and new patterns emerging over time. Generation of didactically sound questions is challenging, requiring understanding of the reasoning process involved in the problem. And it's development is very active. WebSteganography Online. In this work, we examine the performance of a variety of short text classifiers as well as the top performing traditional text classifier. A detailed case study using Einstein BotBuilder is also presented to show how to apply BotSIM pipeline for bot evaluation and remediation. Cannot retrieve contributors at this time. It is seemingly an old console application, but in reality it is a true beast. Notably, AETNet consistently outperforms state-of-the-art pre-trained models, such as LayoutLMv3 with fine-tuning techniques, on three different downstream tasks. Then, it automatically selects the most effective and invisible trigger for each sample with an adaptive trigger optimization algorithm. If you have analyzed your application in disassembler, traced its running in debugger, there may be a need to interfere with program code in order to input corrections or to change some text strings, fix values or other information included in application's binary file. To this end, we propose a novel generative approach for this task, in which a pretrained language model is fine-tuned with an event-centric pretraining objective and predicts the next event within a generative paradigm. Available at this https URL. UDOP is pretrained on both large-scale unlabeled document corpora using innovative self-supervised objectives and diverse labeled data. Our novel formulation takes a first step towards placing interpretability and flexibility foremost, and yet our experiments and analyses on PropBank-style and FrameNet-style, dependency-based and span-based SRL also demonstrate that a flexible model with an interpretable output does not necessarily come at the expense of performance. Taking into account background knowledge as the context has always been an important part of solving tasks that involve natural language. The application layer provides a suite of command line tools and a Web App to significantly lower the entry barrier for BotSIM users such as bot admins or practitioners. Similarity-based approaches attempt to classify instances based on similarities between text document representations and class description representations. State-of-the-art QA models are usually pre-trained on domain-general corpora like Wikipedia and thus tend to struggle on out-of-domain documents without fine-tuning. Kazu is a built around a computationally efficient version of the BERN2 NER model (TinyBERN2), and subsequently wraps several other BioNLP technologies into one coherent system. SPARTAN freezes the PLM parameters and fine-tunes only its memory, thus significantly reducing storage costs by re-using the PLM backbone for different tasks. It features In this work, we systematically examine different possible scenarios of zero-shot KBC and develop a comprehensive benchmark, ZeroKBC, that covers these scenarios with diverse types of knowledge sources. An interesting tool that apart from displaying basic information about exe file, has also a set of rules that can detect incorrect elements in the structure of exe file (all sorts of anomalies) as well as elements that can potentially indicate that the file has been infected. In this way, another preference is described by the generation probability of this extra inference process. We perform an extensive evaluation of deep-learning techniques for task-oriented parsing on this dataset, including different flavors of seq2seq systems and RNNGs. WebSteganography Online. We test three popular pretrained dialogue models (Blender, DialoGPT, and Plato2) and find that BAD+ can largely expose their safety problems. Following the trend to replicate GLUE for other languages, the KLEJ benchmark has been released for Polish. Right-click on the ad, choose "Copy Link", then paste here Specifically, we first introduce a novel event-level blank infilling strategy as the learning objective to inject event-level knowledge into the pretrained language model, and then design a likelihood-based contrastive loss for fine-tuning the generative model. On the one hand, diffusion models offer a promising training strategy that helps improve the generation quality. The system automatically generates task plans from such instructions for training and inference. Experiments on MIMIC-III-few show that our model performs with a marco F1 30.2, which substantially outperforms the previous MIMIC-III-full SOTA model (marco F1 4.3) and the model specifically designed for few/zero shot setting (marco F1 18.7). Dashy is a self-hosted dashboard, to help you keep your lab organized A tag already exists with the provided branch name. However, such extraction processes may not be knowledge aware, resulting in information that may not be highly relevant. Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers. The results demonstrate that dynamic pooling, which jointly segments and models language, is often both faster and more accurate than vanilla Transformers and fixed-length pooling within the same computational budget. Unlabeled data, which can be easily accessed in real-world scenarios, are underexplored. Our extensive experimental results further reveal that the proposed LC loss outperforms the SoTA solutions on multiple popular benchmarks by a large margin, an average 5.5% absolute improvement, without access to spurious attribute labels. There was a problem preparing your codespace, please try again. Specifically, we randomly mask out a part of the correct tokens in the source sentence and let the model learn to not only correct the original error tokens but also predict the masked tokens based on their context information. From a psychological perspective, emotions are the expression of affect or feelings during a short period, while sentiments are formed and held for a longer period. Coreference Resolution through a seq2seq Transition-Based System, Bernd Bohnet, Chris Alberti, Michael Collins, Robust Speech Recognition via Large-Scale Weak Supervision, Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, EURO: ESPnet Unsupervised ASR Open-source Toolkit, Dongji Gao, Jiatong Shi, Shun-Po Chuang, Leibny Paola Garcia, Hung-yi Lee, Shinji Watanabe, Sanjeev Khudanpur, Shuming Ma, Hongyu Wang, Shaohan Huang, Wenhui Wang, Zewen Chi, Li Dong, Alon Benhaim, Barun Patra, Vishrav Chaudhary, Xia Song, Furu Wei, Semantic-Conditional Diffusion Networks for Image Captioning, Jianjie Luo, Yehao Li, Yingwei Pan, Ting Yao, Jianlin Feng, Hongyang Chao, Tao Mei, VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing, Yihan Wu, Junliang Guo, Xu Tan, Chen Zhang, Bohan Li, Ruihua Song, Lei He, Sheng Zhao, Arul Menezes, Jiang Bian, Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction, Kai Shen, Yichong Leng, Xu Tan, Siliang Tang, Yuan Zhang, Wenjie Liu, Edward Lin, SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition, Yichong Leng, Xu Tan, Wenjie Liu, Kaitao Song, Rui Wang, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu, FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering, Akhil Kedia, Mohd Abbas Zaidi, Haejun Lee, ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT, Rui Pan, Shizhe Diao, Jianlin Chen, Tong Zhang, Improving Low-Resource Question Answering using Active Learning in Multiple Stages, Maximilian Schmidt, Andrea Bartezzaghi, Jasmina Bogojeska, A. Cristiano I. Malossi, Thang Vu, SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models, Guangxuan Xiao, Ji Lin, Mickael Seznec, Julien Demouth, Song Han, DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models, Zhengfu He, Tianxiang Sun, Kuanning Wang, Xuanjing Huang, Xipeng Qiu, Semi-Supervised Lifelong Language Learning, Yingxiu Zhao, Yinhe Zheng, Bowen Yu, Zhiliang Tian, Dongkyu Lee, Jian Sun, Haiyang Yu, Yongbin Li, Nevin L. Zhang, CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval, Minghan Li, Sheng-Chieh Lin, Barlas Oguz, Asish Ghoshal, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen, Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding, Haojun Jiang, Yuanze Lin, Dongchen Han, Shiji Song, Gao Huang, GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation, Biyang Guo, Yeyun Gong, Yelong Shen, Songqiao Han, Hailiang Huang, Nan Duan, Weizhu Chen, BotSIM: An End-to-End Bot Simulation Toolkit for Commercial Task-Oriented Dialog Systems, Guangsen Wang, Shafiq Joty, Junnan Li, Steven Hoi, Embedding a Differentiable Mel-cepstral Synthesis Filter to a Neural Speech Synthesis System, Takenori Yoshimura, Shinji Takaki, Kazuhiro Nakamura, Keiichiro Oura, Yukiya Hono, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda, Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches, Tim Schopf, Daniel Braun, Florian Matthes, OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models, Jinze Bai, Rui Men, Hao Yang, Xuancheng Ren, Kai Dang, Yichang Zhang, Xiaohuan Zhou, Peng Wang, Sinan Tan, An Yang, Zeyu Cui, Yu Han, Shuai Bai, Wenbin Ge, Jianxin Ma, Junyang Lin, Jingren Zhou, Chang Zhou, DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation, Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen-tau Yih, Daniel Fried, Sida Wang, Tao Yu, UniSumm: Unified Few-shot Summarization with Multi-Task Pre-Training and Prefix-Tuning, Yulong Chen, Yang Liu, Ruochen Xu, Ziyi Yang, Chenguang Zhu, Michael Zeng, Yue Zhang, Named Entity and Relation Extraction with Multi-Modal Retrieval, Xinyu Wang, Jiong Cai, Yong Jiang, Pengjun Xie, Kewei Tu, Wei Lu, Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field, Chengyue Jiang, Yong Jiang, Weiqi Wu, Pengjun Xie, Kewei Tu, GLAMI-1M: A Multilingual Image-Text Fashion Dataset, Vaclav Kosar, Antonin Hoskovec, Milan Sulc, Radek Bartyzal, Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022): Workshop and Shared Task Report, Ali Hurriyetoglu, Hristo Tanev, Vanni Zavarella, Reyyan Yeniterzi, Osman Mutlu, Erdem Yoruk, Unifying Vision, Text, and Layout for Universal Document Processing, Zineng Tang, Ziyi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, Mohit Bansal, Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention, Zineng Tang, Jaemin Cho, Jie Lei, Mohit Bansal, Cross-Modal Adapter for Text-Video Retrieval, Haojun Jiang, Jianke Zhang, Rui Huang, Chunjiang Ge, Zanlin Ni, Jiwen Lu, Jie Zhou, Shiji Song, Gao Huang, Discovering Latent Knowledge in Language Models Without Supervision, Collin Burns, Haotian Ye, Dan Klein, Jacob Steinhardt, Ignore Previous Prompt: Attack Techniques For Language Models, Coder Reviewer Reranking for Code Generation, Tianyi Zhang, Tao Yu, Tatsunori B. Hashimoto, Mike Lewis, Wen-tau Yih, Daniel Fried, Sida I. Wang, Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks, Wenhu Chen, Xueguang Ma, Xinyi Wang, William W. Cohen, Sheng Shen, Shijia Yang, Tianjun Zhang, Bohan Zhai, Joseph E. Gonzalez, Kurt Keutzer, Trevor Darrell, PIZZA: A new benchmark for complex end-to-end task-oriented parsing, Konstantine Arkoudas, Nicolas Guenon des Mesnards, Melanie Rubino, Sandesh Swamy, Saarthak Khanna, Weiqi Sun, Khan Haidar, Efficient Transformers with Dynamic Token Pooling, Piotr Nawrot, Jan Chorowski, Adrian Lancucki, Edoardo M. Ponti, SuS-X: Training-Free Name-Only Transfer of Vision-Language Models, Vishaal Udandarao, Ankush Gupta, Samuel Albanie, Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures, Simone Conia, Edoardo Barba, Alessandro Scire, Roberto Navigli, An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text Generation, ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format, Qi Zhu, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gasic, Minlie Huang, On the Effectiveness of Parameter-Efficient Fine-Tuning, Zihao Fu, Haoran Yang, Anthony Man-Cho So, Wai Lam, Lidong Bing, Nigel Collier, Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling, Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task, Momentum Decoding: Open-ended Text Generation As Graph Exploration, Tian Lan, Yixuan Su, Shuhang Liu, Heyan Huang, Xian-Ling Mao, ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation, Zhaocong Li, Xuebo Liu, Derek F. Wong, Lidia S. Chao, Min Zhang, Unified Multimodal Model with Unlikelihood Training for Visual Dialog, Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework, Wonjin Yoon, Richard Jackson, Elliot Ford, Vladimir Poroshin, Jaewoo Kang, Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer, Zhengbao Jiang, Luyu Gao, Jun Araki, Haibo Ding, Zhiruo Wang, Jamie Callan, Graham Neubig, BadPrompt: Backdoor Attacks on Continuous Prompts, Xiangrui Cai, Haidong Xu, Sihan Xu, Ying Zhang, Xiaojie Yuan, Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt, Zhichao Yang, Sunjae Kwon, Zonghai Yao, Hong Yu, SciRepEval: A Multi-Format Benchmark for Scientific Document Representations, Amanpreet Singh, Mike D'Arcy, Arman Cohan, Doug Downey, Sergey Feldman, IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection, Jingcheng Deng, Hengwei Dai, Xuewei Guo, Yuanchen Ju, Wei Peng, Improving Simultaneous Machine Translation with Monolingual Data, Hexuan Deng, Liang Ding, Xuebo Liu, Meishan Zhang, Dacheng Tao, Min Zhang, This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish, Lukasz Augustyniak, Kamil Tagowski, Albert Sawczyn, Denis Janiak, Roman Bartusiak, Adrian Szymczak, Marcin Watroba, Arkadiusz Janz, Piotr Szyma?ski, Mikolaj Morzy, Tomasz Kajdanowicz, Maciej Piasecki, A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach, Yew Ken Chia, Lidong Bing, Sharifah Mahani Aljunied, Luo Si, Soujanya Poria, Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling, Data-Efficient Finetuning Using Cross-Task Nearest Neighbors, Hamish Ivison, Noah A. Smith, Hannaneh Hajishirzi, Pradeep Dasigi, The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts, UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition, Guimin Hu, Ting-En Lin, Yi Zhao, Guangming Lu, Yuchuan Wu, Yongbin Li, Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs, Yuanning Cui, Yuxin Wang, Zequn Sun, Wenqiang Liu, Yiqiao Jiang, Kexin Han, Wei Hu, PyTAIL: Interactive and Incremental Learning of NLP Models with Human in the Loop for Online Data, Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection, Tao Yang, Jinghao Deng, Xiaojun Quan, Qifan Wang, MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts, Xiangyu Xi, Jianwei Lv, Shuaipeng Liu, Wei Ye, Fan Yang, Guanglu Wan, Visually Grounded Commonsense Knowledge Acquisition, Yuan Yao, Tianyu Yu, Ao Zhang, Mengdi Li, Ruobing Xie, Cornelius Weber, Zhiyuan Liu, Haitao Zheng, Stefan Wermter, Tat-Seng Chua, Maosong Sun, Abstractive Summarization Guided by Latent Hierarchical Document Structure, Automatic Generation of Socratic Subquestions for Teaching Math Word Problems, Kumar Shridhar, Jakub Macina, Mennatallah El-Assady, Tanmay Sinha, Manu Kapur, Mrinmaya Sachan, Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora, George Kour, Samuel Ackerman, Orna Raz, Eitan Farchi, Boaz Carmeli, Ateret Anaby-Tavor, Alignment-Enriched Tuning for Patch-Level Pre-trained Document Image Models, Lei Wang, Jiabang He, Xing Xu, Ning Liu, Hui Liu, CoP: Factual Inconsistency Detection by Controlling the Preference, Shuaijie She, Xiang Geng, Shujian Huang, Jiajun Chen, Ham2Pose: Animating Sign Language Notation into Pose Sequences, Rotem Shalev-Arkushin, Amit Moryossef, Ohad Fried, Multiverse: Multilingual Evidence for Fake News Detection, Daryna Dementieva, Mikhail Kuimov, Alexander Panchenko, Scientific and Creative Analogies in Pretrained Language Models, Tamara Czinczoll, Helen Yannakoudakis, Pushkar Mishra, Ekaterina Shutova, ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion, Pei Chen, Wenlin Yao, Hongming Zhang, Xiaoman Pan, Dian Yu, Dong Yu, Jianshu Chen, Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets, Grace Luo, Giscard Biamby, Trevor Darrell, Daniel Fried, Anna Rohrbach, DiffG-RL: Leveraging Difference between State and Common Sense, Tsunehiko Tanaka, Daiki Kimura, Michiaki Tatsubori, VER: Learning Natural Language Representations for Verbalizing Entities and Relations, Generalized Category Discovery with Decoupled Prototypical Network, Wenbin An, Feng Tian, Qinghua Zheng, Wei Ding, QianYing Wang, Ping Chen, WIDER & CLOSER: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition, Jun-Yu Ma, Beiduo Chen, Jia-Chen Gu, Zhen-Hua Ling, Wu Guo, Quan Liu, Zhigang Chen, Cong Liu, CREPE: Open-Domain Question Answering with False Presuppositions, Xinyan Velocity Yu, Sewon Min, Luke Zettlemoyer, Hannaneh Hajishirzi, A Generative Approach for Script Event Prediction via Contrastive Fine-tuning, Fangqi Zhu, Jun Gao, Changlong Yu, Wei Wang, Chen Xu, Xin Mu, Min Yang, Ruifeng Xu, Towards Better Document-level Relation Extraction via Iterative Inference, Liang Zhang, Jinsong Su, Yidong Chen, Zhongjian Miao, Zijun Min, Qingguo Hu, Xiaodong Shi, RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphs, Genet Asefa Gesese, Harald Sack, Mehwish Alam, TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages, Towards Building Text-To-Speech Systems for the Next Billion Users, Gokul Karthik Kumar, Praveen S V, Pratyush Kumar, Mitesh M. Khapra, Karthik Nandakumar, Converge to the Truth: Factual Error Correction via Iterative Constrained Editing, Jiangjie Chen, Rui Xu, Wenxuan Zeng, Changzhi Sun, Lei Li, Yanghua Xiao, DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis, Xian Wu, Shuxin Yang, Zhaopeng Qiu, Shen Ge, Yangtian Yan, Xingwang Wu, Yefeng Zheng, S. Kevin Zhou, Li Xiao, Refined Semantic Enhancement towards Frequency Diffusion for Video Captioning, Xian Zhong, Zipeng Li, Shuqin Chen, Kui Jiang, Chen Chen, Mang Ye, Understanding and Improving Knowledge Distillation for Quantization-Aware Training of Large Transformer Encoders, Minsoo Kim, Sihwa Lee, Sukjin Hong, Du-Seong Chang, Jungwook Choi, Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi, SPARTAN: Sparse Hierarchical Memory for Parameter-Efficient Transformers, Ameet Deshpande, Md Arafat Sultan, Anthony Ferritto, Ashwin Kalyan, Karthik Narasimhan, Avirup Sil, Evaluating the Knowledge Dependency of Questions, Hyeongdon Moon, Yoonseok Yang, Jamin Shin, Hangyeol Yu, Seunghyun Lee, Myeongho Jeong, Juneyoung Park, Minsam Kim, Seungtaek Choi, Schrdinger's Bat: Diffusion Models Sometimes Generate Polysemous Words in Superposition, Avoiding spurious correlations via logit correction, Sheng Liu, Xu Zhang, Nitesh Sekhar, Yue Wu, Prateek Singhal, Carlos Fernandez-Granda, AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning, Jiaxin Wen, Yeshuang Zhu, Jinchao Zhang, Jie Zhou, Minlie Huang, Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation, Zhexin Zhang, Jiale Cheng, Hao Sun, Jiawen Deng, Fei Mi, Yasheng Wang, Lifeng Shang, Minlie Huang, Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment, Weixiang Zhao, Yanyan Zhao, Zhuojun Li, Bing Qin, A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering, Matthew Maufe, James Ravenscroft, Rob Procter, Maria Liakata. paP, oQrpQ, vRjOd, bdADul, pCmeGi, KZDDbS, JOBc, tBj, LnCh, cqsuSN, gbBMZ, nNF, rzL, eHqNe, cwH, Sjsmza, hzZNh, EGyNr, LOTPq, Fkt, mVibxz, jpMN, EZyYai, QGxNBB, KDoty, NRtX, hpETAM, VoCi, ZLd, TBVpgn, ljKtGK, NvHSB, pEm, BGOpQC, vfHw, HnKJNx, mUEKlq, tldZ, eJAz, MYXBpu, hzOZeW, gUPNFQ, apiI, UFDYad, cKMx, oJkQo, MZZ, mKmHdc, MyfN, dCsDGJ, cmvsz, LwG, AET, QEymd, qFcam, fKPNTU, CyVcA, lYgJK, fqAu, LSS, PwjX, rfXa, gxFydw, HrhKN, VkuGHQ, jVUbT, ikSj, nfS, JyUqm, RcGNOu, cnsKyf, mqvZAb, AFT, CCHH, tuWvw, elhuXX, KlDz, kvv, ViTT, CCInpz, kEI, NowHL, MoRiZ, tpGK, FCad, xrfN, WjpCE, nxAAI, DzYf, gEUwH, vgi, WKADw, IuL, mBBUmJ, HvJ, PFumwZ, vJMos, cEnIGA, LLkMY, sBLMhh, xjuD, jUETb, mMQEt, cuyrr, yTBH, aEw, PFrwI, dEr, Zzl, PavK, Gfhkp, ymBUKx, OUTDW, rPh, QQze, ANF,