pwc-archive/papers-with-abstracts huggingface dataset:
Learning user-defined sub-goals using memory editing in reinforcement learning
Authors: GyeongTaek Lee
Date: 2022-05-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

The aim of reinforcement learning (RL) is to allow the agent to achieve the final goal. Most RL studies have focused on improving the efficiency of learning to achieve the final goal faster. However, the RL model is very difficult to modify an intermediate route in the process of reaching the final goal. That is, the agent cannot be under control to achieve other sub-goals in the existing studies. If the agent can go through the sub-goals on the way to the destination, the RL can be applied and studied in various fields. In this study, I propose a methodology to achieve the user-defined sub-goals as well as the final goal using memory editing. The memory editing is performed to generate various sub-goals and give an additional reward to the agent. In addition, the sub-goals are separately learned from the final goal. I set two simple environments and various scenarios in the test environments. As a result, the agent almost successfully passed the sub-goals as well as the final goal under control. Moreover, the agent was able to be induced to visit the novel state indirectly in the environments. I expect that this methodology can be used in the fields that need to control the agent in a variety of scenarios.
Geometric Graph Representation with Learnable Graph Structure and Adaptive AU Constraint for Micro-Expression Recognition
Authors: Jinsheng Wei, Wei Peng, Guanming Lu, Yante Li, Jingjie Yan, Guoying Zhao
Date: 2022-05-01 00:00:00
Tasks: Micro Expression Recognition, Micro-Expression Recognition, Self-Learning

Micro-expression recognition (MER) is valuable because micro-expressions (MEs) can reveal genuine emotions. Most works take image sequences as input and cannot effectively explore ME information because subtle ME-related motions are easily submerged in unrelated information. Instead, the facial landmark is a low-dimensional and compact modality, which achieves lower computational cost and potentially concentrates on ME-related movement features. However, the discriminability of facial landmarks for MER is unclear. Thus, this paper explores the contribution of facial landmarks and proposes a novel framework to efficiently recognize MEs. Firstly, a geometric two-stream graph network is constructed to aggregate the low-order and high-order geometric movement information from facial landmarks to obtain discriminative ME representation. Secondly, a self-learning fashion is introduced to automatically model the dynamic relationship between nodes even long-distance nodes. Furthermore, an adaptive action unit loss is proposed to reasonably build the strong correlation between landmarks, facial action units and MEs. Notably, this work provides a novel idea with much higher efficiency to promote MER, only utilizing graph-based geometric features. The experimental results demonstrate that the proposed method achieves competitive performance with a significantly reduced computational cost. Furthermore, facial landmarks significantly contribute to MER and are worth further study for high-efficient ME analysis.
Visual Spatial Reasoning
Authors: Fangyu Liu, Guy Emerson, Nigel Collier
Date: 2022-04-30 00:00:00
Tasks: Spatial Reasoning, Visual Entailment, Visual Reasoning

Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and-language models (VLMs) struggle to capture relational information. In this paper, we present Visual Spatial Reasoning (VSR), a dataset containing more than 10k natural text-image pairs with 66 types of spatial relations in English (such as: under, in front of, and facing). While using a seemingly simple annotation format, we show how the dataset includes challenging linguistic phenomena, such as varying reference frames. We demonstrate a large gap between human and model performance: the human ceiling is above 95%, while state-of-the-art models only achieve around 70%. We observe that VLMs' by-relation performances have little correlation with the number of training examples and the tested models are in general incapable of recognising relations concerning the orientations of objects.
Combined Learning of Neural Network Weights for Privacy in Collaborative Tasks
Authors: Aline R. Ioste, Alan M. Durham, Marcelo Finger
Date: 2022-04-30 00:00:00

We introduce CoLN, Combined Learning of Neural network weights, a novel method to securely combine Machine Learning models over sensitive data with no sharing of data. With CoLN, local hosts use the same Neural Network architecture and base parameters to train a model using only locally available data. Locally trained models are then submitted to a combining agent, which produces a combined model. The new model's parameters can be sent back to hosts, and can then be used as initial parameters for a new training iteration. CoLN is capable of combining several distributed neural networks of the same kind but is not restricted to any single neural architecture. In this paper we detail the combination algorithm and present experiments with feed-forward, convolutional, and recurrent Neural Network architectures, showing that the CoLN combined model approximates the performance of a hypothetical ideal centralized model, trained using the combination of the local datasets. CoLN can contribute to secure collaborative research, as required in the medical area, where privacy issues preclude data sharing, but where the limitations of local data demand information derived from larger datasets.
LayoutBERT: Masked Language Layout Model for Object Insertion
Authors: Kerem Turgutlu, Sanat Sharma, Jayant Kumar
Date: 2022-04-30 00:00:00
Tasks: Language Modelling, model, Object, Retrieval

Image compositing is one of the most fundamental steps in creative workflows. It involves taking objects/parts of several images to create a new image, called a composite. Currently, this process is done manually by creating accurate masks of objects to be inserted and carefully blending them with the target scene or images, usually with the help of tools such as Photoshop or GIMP. While there have been several works on automatic selection of objects for creating masks, the problem of object placement within an image with the correct position, scale, and harmony remains a difficult problem with limited exploration. Automatic object insertion in images or designs is a difficult problem as it requires understanding of the scene geometry and the color harmony between objects. We propose LayoutBERT for the object insertion task. It uses a novel self-supervised masked language model objective and bidirectional multi-head self-attention. It outperforms previous layout-based likelihood models and shows favorable properties in terms of model capacity. We demonstrate the effectiveness of our approach for object insertion in the image compositing setting and other settings like documents and design templates. We further demonstrate the usefulness of the learned representations for layout-based retrieval tasks. We provide both qualitative and quantitative evaluations on datasets from diverse domains like COCO, PublayNet, and two new datasets which we call Image Layouts and Template Layouts. Image Layouts which consists of 5.8 million images with layout annotations is the largest image layout dataset to our knowledge. We also share ablation study results on the effect of dataset size, model size and class sample size for this task.
Opponent Modeling in Negotiation Dialogues by Related Data Adaptation
Authors: Kushal Chawla, Gale M. Lucas, Jonathan May, Jonathan Gratch
Date: 2022-04-30 00:00:00
Proceeding: Findings (NAACL) 2022 7

Opponent modeling is the task of inferring another party's mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training. In this work, we propose a ranker for identifying these priorities from negotiation dialogues. The model takes in a partial dialogue as input and predicts the priority order of the opponent. We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent's preferences and offers, as a proxy to relying on granular utterance-level annotations. We show the utility of our proposed approach through extensive experiments based on two dialogue datasets. We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios. Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent. We release our code to support future work in this direction.
Engineering flexible machine learning systems by traversing functionally-invariant paths
Authors: Guruprasad Raghavan, Bahey Tharwat, Surya Narayanan Hari, Dhruvil Satani, Matt Thomson
Date: 2022-04-30 00:00:00
Tasks: Adversarial Robustness, Continual Learning, Decision Making, Image Classification, Instruction Following, Question Answering

Transformers have emerged as the state of the art neural network architecture for natural language processing and computer vision. In the foundation model paradigm, large transformer models (BERT, GPT3/4, Bloom, ViT) are pre-trained on self-supervised tasks such as word or image masking, and then, adapted through fine-tuning for downstream user applications including instruction following and Question Answering. While many approaches have been developed for model fine-tuning including low-rank weight update strategies (eg. LoRA), underlying mathematical principles that enable network adaptation without knowledge loss remain poorly understood. Here, we introduce a differential geometry framework, functionally invariant paths (FIP), that provides flexible and continuous adaptation of neural networks for a range of machine learning goals and network sparsification objectives. We conceptualize the weight space of a neural network as a curved Riemannian manifold equipped with a metric tensor whose spectrum defines low rank subspaces in weight space that accommodate network adaptation without loss of prior knowledge. We formalize adaptation as movement along a geodesic path in weight space while searching for networks that accommodate secondary objectives. With modest computational resources, the FIP algorithm achieves comparable to state of the art performance on continual learning and sparsification tasks for language models (BERT), vision transformers (ViT, DeIT), and the CNNs. Broadly, we conceptualize a neural network as a mathematical object that can be iteratively transformed into distinct configurations by the path-sampling algorithm to define a sub-manifold of weight space that can be harnessed to achieve user goals.
Continual Learning with Foundation Models: An Empirical Study of Latent Replay
Authors: Oleksiy Ostapenko, Timothee Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin
Date: 2022-04-30 00:00:00
Tasks: Benchmarking, Continual Learning

Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a variety of downstream tasks and domains. Motivated by this, we study the efficacy of pre-trained vision models as a foundation for downstream continual learning (CL) scenarios. Our goal is twofold. First, we want to understand the compute-accuracy trade-off between CL in the raw-data space and in the latent space of pre-trained encoders. Second, we investigate how the characteristics of the encoder, the pre-training algorithm and data, as well as of the resulting latent space affect CL performance. For this, we compare the efficacy of various pre-trained models in large-scale benchmarking scenarios with a vanilla replay setting applied in the latent and in the raw-data space. Notably, this study shows how transfer, forgetting, task similarity and learning are dependent on the input data characteristics and not necessarily on the CL algorithms. First, we show that under some circumstances reasonable CL performance can readily be achieved with a non-parametric classifier at negligible compute. We then show how models pre-trained on broader data result in better performance for various replay sizes. We explain this with representational similarity and transfer properties of these representations. Finally, we show the effectiveness of self-supervised pre-training for downstream domains that are out-of-distribution as compared to the pre-training domain. We point out and validate several research directions that can further increase the efficacy of latent CL including representation ensembling. The diverse set of datasets used in this study can serve as a compute-efficient playground for further CL research. The codebase is available under https://github.com/oleksost/latent_CL.
Artificial Intelligence and Medicine: A literature review
Authors: Chottiwatt Jittprasong
Date: 2022-04-30 00:00:00

In practically every industry today, artificial intelligence is one of the most effective ways for machines to assist humans. Since its inception, a large number of researchers throughout the globe have been pioneering the application of artificial intelligence in medicine. Although artificial intelligence may seem to be a 21st-century concept, Alan Turing pioneered the first foundation concept in the 1940s. Artificial intelligence in medicine has a huge variety of applications that researchers are continually exploring. The tremendous increase in computer and human resources has hastened progress in the 21st century, and it will continue to do so for many years to come. This review of the literature will highlight the emerging field of artificial intelligence in medicine and its current level of development.
A Survey of Machine Narrative Reading Comprehension Assessments
Authors: Yisi Sang, Xiangyang Mou, Jing Li, Jeffrey Stanton, Mo Yu
Date: 2022-04-30 00:00:00
Tasks: Reading Comprehension, Survey

As the body of research on machine narrative comprehension grows, there is a critical need for consideration of performance assessment strategies as well as the depth and scope of different benchmark tasks. Based on narrative theories, reading comprehension theories, as well as existing machine narrative reading comprehension tasks and datasets, we propose a typology that captures the main similarities and differences among assessment tasks; and discuss the implications of our typology for new task design and the challenges of narrative reading comprehension.
Leveraging Emotion-specific Features to Improve Transformer Performance for Emotion Classification
Authors: Shaily Desai, Atharva Kshirsagar, Aditi Sidnerlikar, Nikhil Khodake, Manisha Marathe
Date: 2022-04-30 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Articles, Classification, Emotion Classification

This paper describes the approach to the Emotion Classification shared task held at WASSA 2022 by team PVGs AI Club. This Track 2 sub-task focuses on building models which can predict a multi-class emotion label based on essays from news articles where a person, group or another entity is affected. Baseline transformer models have been demonstrating good results on sequence classification tasks, and we aim to improve this performance with the help of ensembling techniques, and by leveraging two variations of emotion-specific representations. We observe better results than our baseline models and achieve an accuracy of 0.619 and a macro F1 score of 0.520 on the emotion classification task.
PGD: A Large-scale Professional Go Dataset for Data-driven Analytics
Authors: Yifan Gao
Date: 2022-04-30 00:00:00
Tasks: Board Games

Lee Sedol is on a winning streak--does this legend rise again after the competition with AlphaGo? Ke Jie is invincible in the world championship--can he still win the title this time? Go is one of the most popular board games in East Asia, with a stable professional sports system that has lasted for decades in China, Japan, and Korea. There are mature data-driven analysis technologies for many sports, such as soccer, basketball, and esports. However, developing such technology for Go remains nontrivial and challenging due to the lack of datasets, meta-information, and in-game statistics. This paper creates the Professional Go Dataset (PGD), containing 98,043 games played by 2,148 professional players from 1950 to 2021. After manual cleaning and labeling, we provide detailed meta-information for each player, game, and tournament. Moreover, the dataset includes analysis results for each move in the match evaluated by advanced AlphaZero-based AI. To establish a benchmark for PGD, we further analyze the data and extract meaningful in-game features based on prior knowledge related to Go that can indicate the game status. With the help of complete meta-information and constructed in-game features, our results prediction system achieves an accuracy of 75.30%, much higher than several state-of-the-art approaches (64%-65%). As far as we know, PGD is the first dataset for data-driven analytics in Go and even in board games. Beyond this promising result, we provide more examples of tasks that benefit from our dataset. The ultimate goal of this paper is to bridge this ancient game and the modern data science community. It will advance research on Go-related analytics to enhance the fan experience, help players improve their ability, and facilitate other promising aspects. The dataset will be made publicly available.
A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
Authors: Runxin Xu, Peiyi Wang, Tianyu Liu, Shuang Zeng, Baobao Chang, Zhifang Sui
Date: 2022-04-30 00:00:00
Proceeding: NAACL 2022 7
Tasks: Abstract Meaning Representation, Document-level Event Extraction, Event Argument Extraction, Event Extraction, Sentence

Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored. In this paper, we focus on extracting event arguments from an entire document, which mainly faces two critical problems: a) the long-distance dependency between trigger and arguments over sentences; b) the distracting context towards an event in the document. To address these issues, we propose a Two-Stream Abstract meaning Representation enhanced extraction model (TSAR). TSAR encodes the document from different perspectives by a two-stream encoding module, to utilize local and global information and lower the impact of distracting context. Besides, TSAR introduces an AMR-guided interaction module to capture both intra-sentential and inter-sentential features, based on the locally and globally constructed AMR semantic graphs. An auxiliary boundary loss is introduced to enhance the boundary information for text spans explicitly. Extensive experiments illustrate that TSAR outperforms previous state-of-the-art by a large margin, with 2.54 F1 and 5.13 F1 performance gain on the public RAMS and WikiEvents datasets respectively, showing the superiority in the cross-sentence arguments extraction. We release our code in https://github.com/ PKUnlp-icler/TSAR.
An attention model for the formation of collectives in real-world domains
Authors: Adrià Fenoy, Filippo Bistaffa, Alessandro Farinelli
Date: 2022-04-30 00:00:00
Tasks: Decoder

We consider the problem of forming collectives of agents for real-world applications aligned with Sustainable Development Goals (e.g., shared mobility, cooperative learning). We propose a general approach for the formation of collectives based on a novel combination of an attention model and an integer linear program (ILP). In more detail, we propose an attention encoder-decoder model that transforms a collective formation instance to a weighted set packing problem, which is then solved by an ILP. Results on two real-world domains (i.e., ridesharing and team formation for cooperative learning) show that our approach provides solutions that are comparable (in terms of quality) to the ones produced by state-of-the-art approaches specific to each domain. Moreover, our solution outperforms the most recent general approach for forming collectives based on Monte Carlo tree search.
Software Testing for Machine Learning
Authors: Dusica Marijan, Arnaud Gotlieb
Date: 2022-04-30 00:00:00
Tasks: BIG-bench Machine Learning, software testing

Machine learning has become prevalent across a wide variety of applications. Unfortunately, machine learning has also shown to be susceptible to deception, leading to errors, and even fatal failures. This circumstance calls into question the widespread use of machine learning, especially in safety-critical applications, unless we are able to assure its correctness and trustworthiness properties. Software verification and testing are established technique for assuring such properties, for example by detecting errors. However, software testing challenges for machine learning are vast and profuse - yet critical to address. This summary talk discusses the current state-of-the-art of software testing for machine learning. More specifically, it discusses six key challenge areas for software testing of machine learning systems, examines current approaches to these challenges and highlights their limitations. The paper provides a research agenda with elaborated directions for making progress toward advancing the state-of-the-art on testing of machine learning.
Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods
Authors: Takane Ueno, Yuto Sawa, Yeongdae Kim, Jacqueline Urakami, Hiroki Oura, Katie Seaborn
Date: 2022-04-30 00:00:00

Trust has emerged as a key factor in people's interactions with AI-infused systems. Yet, little is known about what models of trust have been used and for what systems: robots, virtual characters, smart vehicles, decision aids, or others. Moreover, there is yet no known standard approach to measuring trust in AI. This scoping review maps out the state of affairs on trust in human-AI interaction (HAII) from the perspectives of models, measures, and methods. Findings suggest that trust is an important and multi-faceted topic of study within HAII contexts. However, most work is under-theorized and under-reported, generally not using established trust models and missing details about methods, especially Wizard of Oz. We offer several targets for systematic review work as well as a research agenda for combining the strengths and addressing the weaknesses of the current literature.