pwc-archive/papers-with-abstracts huggingface dataset:
Advancing Semi-Supervised Task Oriented Dialog Systems by JSA Learning of Discrete Latent Variable Models
Authors: Yucheng Cai, Hong Liu, Zhijian Ou, Yi Huang, Junlan Feng
Date: 2022-07-25 00:00:00
Proceeding: SIGDIAL (ACL) 2022 9

Developing semi-supervised task-oriented dialog (TOD) systems by leveraging unlabeled dialog data has attracted increasing interests. For semi-supervised learning of latent state TOD models, variational learning is often used, but suffers from the annoying high-variance of the gradients propagated through discrete latent variables and the drawback of indirectly optimizing the target log-likelihood. Recently, an alternative algorithm, called joint stochastic approximation (JSA), has emerged for learning discrete latent variable models with impressive performances. In this paper, we propose to apply JSA to semi-supervised learning of the latent state TOD models, which is referred to as JSA-TOD. To our knowledge, JSA-TOD represents the first work in developing JSA based semi-supervised learning of discrete latent variable conditional models for such long sequential generation problems like in TOD systems. Extensive experiments show that JSA-TOD significantly outperforms its variational learning counterpart. Remarkably, semi-supervised JSA-TOD using 20% labels performs close to the full-supervised baseline on MultiWOZ2.1.
What makes you change your mind? An empirical investigation in online group decision-making conversations
Authors: Georgi Karadzhov, Tom Stafford, Andreas Vlachos
Date: 2022-07-25 00:00:00
Proceeding: SIGDIAL (ACL) 2022 9
Tasks: Change Point Detection, Decision Making, Learning-To-Rank, text-classification, Text Classification

People leverage group discussions to collaborate in order to solve complex tasks, e.g. in project meetings or hiring panels. By doing so, they engage in a variety of conversational strategies where they try to convince each other of the best approach and ultimately reach a decision. In this work, we investigate methods for detecting what makes someone change their mind. To this end, we leverage a recently introduced dataset containing group discussions of people collaborating to solve a task. To find out what makes someone change their mind, we incorporate various techniques such as neural text classification and language-agnostic change point detection. Evaluation of these methods shows that while the task is not trivial, the best way to approach it is using a language-aware model with learning-to-rank training. Finally, we examine the cues that the models develop as indicative of the cause of a change of mind.
Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent
Authors: Ethan A. Chi, Ashwin Paranjape, Abigail See, Caleb Chiam, Trenton Chang, Kathleen Kenealy, Swee Kiat Lim, Amelia Hardy, Chetanya Rastogi, Haojun Li, Alexander Iyabor, Yutong He, Hari Sowrirajan, Peng Qi, Kaushik Ram Sadagopan, Nguyet Minh Phu, Dilara Soylu, Jillian Tang, Avanika Narayan, Giovanni Campagna, Christopher D. Manning
Date: 2022-07-25 00:00:00
Proceeding: SIGDIAL (ACL) 2022 9
Tasks: Chatbot

We present Chirpy Cardinal, an open-domain social chatbot. Aiming to be both informative and conversational, our bot chats with users in an authentic, emotionally intelligent way. By integrating controlled neural generation with scaffolded, hand-written dialogue, we let both the user and bot take turns driving the conversation, producing an engaging and socially fluent experience. Deployed in the fourth iteration of the Alexa Prize Socialbot Grand Challenge, Chirpy Cardinal handled thousands of conversations per day, placing second out of nine bots with an average user rating of 3.58/5.
Learning a Dual-Mode Speech Recognition Model via Self-Pruning
Authors: Chunxi Liu, Yuan Shangguan, Haichuan Yang, Yangyang Shi, Raghuraman Krishnamoorthi, Ozlem Kalinli
Date: 2022-07-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Self-Supervised Learning, speech-recognition, Speech Recognition

There is growing interest in unifying the streaming and full-context automatic speech recognition (ASR) networks into a single end-to-end ASR model to simplify the model training and deployment for both use cases. While in real-world ASR applications, the streaming ASR models typically operate under more storage and computational constraints - e.g., on embedded devices - than any server-side full-context models. Motivated by the recent progress in Omni-sparsity supernet training, where multiple subnetworks are jointly optimized in one single model, this work aims to jointly learn a compact sparse on-device streaming ASR model, and a large dense server non-streaming model, in a single supernet. Next, we present that, performing supernet training on both wav2vec 2.0 self-supervised learning and supervised ASR fine-tuning can not only substantially improve the large non-streaming model as shown in prior works, and also be able to improve the compact sparse streaming model.
Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2020
Authors: Maaz Amjad, Grigori Sidorov, Alisa Zhila, Alexander Gelbukh, Paolo Rosso
Date: 2022-07-25 00:00:00
Tasks: Articles, BIG-bench Machine Learning, Binary Classification, Fake News Detection

This overview paper describes the first shared task on fake news detection in Urdu language. The task was posed as a binary classification task, in which the goal is to differentiate between real and fake news. We provided a dataset divided into 900 annotated news articles for training and 400 news articles for testing. The dataset contained news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6 different countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for the task. 9 teams submitted their experimental results. The participants used various machine learning methods ranging from feature-based traditional machine learning to neural networks techniques. The best performing system achieved an F-score value of 0.90, showing that the BERT-based approach outperforms other machine learning techniques
Enhancements to the BOUN Treebank Reflecting the Agglutinative Nature of Turkish
Authors: Büşra Marşan, Salih Furkan Akkurt, Muhammet Şen, Merve Gürbüz, Onur Güngör, Şaziye Betül Özateş, Suzan Üsküdarlı, Arzucan Özgür, Tunga Güngör, Balkız Öztürk
Date: 2022-07-24 00:00:00
Tasks: Miscellaneous

In this study, we aim to offer linguistically motivated solutions to resolve the issues of the lack of representation of null morphemes, highly productive derivational processes, and syncretic morphemes of Turkish in the BOUN Treebank without diverging from the Universal Dependencies framework. In order to tackle these issues, new annotation conventions were introduced by splitting certain lemmas and employing the MISC (miscellaneous) tab in the UD framework to denote derivation. Representational capabilities of the re-annotated treebank were tested on a LSTM-based dependency parser and an updated version of the BoAT Tool is introduced.
Towards a Sentiment-Aware Conversational Agent
Authors: Isabel Dias, Ricardo Rei, Patrícia Pereira, Luisa Coheur
Date: 2022-07-24 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification, Text Generation

In this paper, we propose an end-to-end sentiment-aware conversational agent based on two models: a reply sentiment prediction model, which leverages the context of the dialogue to predict an appropriate sentiment for the agent to express in its reply; and a text generation model, which is conditioned on the predicted sentiment and the context of the dialogue, to produce a reply that is both context and sentiment appropriate. Additionally, we propose to use a sentiment classification model to evaluate the sentiment expressed by the agent during the development of the model. This allows us to evaluate the agent in an automatic way. Both automatic and human evaluation results show that explicitly guiding the text generation model with a pre-defined set of sentences leads to clear improvements, both regarding the expressed sentiment and the quality of the generated text.
Better Reasoning Behind Classification Predictions with BERT for Fake News Detection
Authors: Daesoo Lee
Date: 2022-07-23 00:00:00
Tasks: Classification, Fake News Detection, Self-Supervised Learning

Fake news detection has become a major task to solve as there has been an increasing number of fake news on the internet in recent years. Although many classification models have been proposed based on statistical learning methods showing good results, reasoning behind the classification performances may not be enough. In the self-supervised learning studies, it has been highlighted that a quality of representation (embedding) space matters and directly affects a downstream task performance. In this study, a quality of the representation space is analyzed visually and analytically in terms of linear separability for different classes on a real and fake news dataset. To further add interpretability to a classification model, a modification of Class Activation Mapping (CAM) is proposed. The modified CAM provides a CAM score for each word token, where the CAM score on a word token denotes a level of focus on that word token to make the prediction. Finally, it is shown that the naive BERT model topped with a learnable linear layer is enough to achieve robust performance while being compatible with CAM.
Vaccine Discourse on Twitter During the COVID-19 Pandemic
Authors: Gabriel Lindelöf, Talayeh Aledavood, Barbara Keller
Date: 2022-07-23 00:00:00

Since the onset of the COVID-19 pandemic, vaccines have been an important topic in public discourse. The discussions around vaccines are polarized as some see them as an important measure to end the pandemic, and others are hesitant or find them harmful. This study investigates posts related to COVID-19 vaccines on Twitter and focuses on those which have a negative stance toward vaccines. A dataset of 16,713,238 English tweets related to COVID-19 vaccines was collected covering the period from March 1, 2020, to July 31, 2021. We used the Scikit-learn Python library to apply a support vector machine (SVM) classifier to identify the tweets with a negative stance toward the COVID-19 vaccines. A total of 5,163 tweets were used to train the classifier, out of which a subset of 2,484 tweets were manually annotated by us and made publicly available. We used the BERTtopic model to extract and investigate the topics discussed within the negative tweets and how they changed over time. We show that the negativity with respect to COVID-19 vaccines has decreased over time along with the vaccine roll-outs. We identify 37 topics of discussion and present their respective importance over time. We show that popular topics consist of conspiratorial discussions such as 5G towers and microchips, but also contain legitimate concerns around vaccination safety and side effects as well as concerns about policies. Our study shows that even unpopular opinions or conspiracy theories can become widespread when paired with a widely popular discussion topic such as COVID-19 vaccines. Understanding the concerns and the discussed topics and how they change over time is essential for policymakers and public health authorities to provide better and in-time information and policies, to facilitate vaccination of the population in future similar crises.
Catch Me If You Can: Deceiving Stance Detection and Geotagging Models to Protect Privacy of Individuals on Twitter
Authors: Dilara Dogan, Bahadir Altun, Muhammed Said Zengin, Mucahid Kutlu, Tamer Elsayed
Date: 2022-07-23 00:00:00
Tasks: Stance Detection

The recent advances in natural language processing have yielded many exciting developments in text analysis and language understanding models; however, these models can also be used to track people, bringing severe privacy concerns. In this work, we investigate what individuals can do to avoid being detected by those models while using social media platforms. We ground our investigation in two exposure-risky tasks, stance detection and geotagging. We explore a variety of simple techniques for modifying text, such as inserting typos in salient words, paraphrasing, and adding dummy social media posts. Our experiments show that the performance of BERT-based models fined tuned for stance detection decreases significantly due to typos, but it is not affected by paraphrasing. Moreover, we find that typos have minimal impact on state-of-the-art geotagging models due to their increased reliance on social networks; however, we show that users can deceive those models by interacting with different users, reducing their performance by almost 50%.
Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs
Authors: Yuxin Wang, Yuanning Cui, Wenqiang Liu, Zequn Sun, Yiqiao Jiang, Kexin Han, Wei Hu
Date: 2022-07-23 00:00:00
Tasks: Entity Alignment, Inductive Learning, Knowledge Graphs

Entity alignment is a basic and vital technique in knowledge graph (KG) integration. Over the years, research on entity alignment has resided on the assumption that KGs are static, which neglects the nature of growth of real-world KGs. As KGs grow, previous alignment results face the need to be revisited while new entity alignment waits to be discovered. In this paper, we propose and dive into a realistic yet unexplored setting, referred to as continual entity alignment. To avoid retraining an entire model on the whole KGs whenever new entities and triples come, we present a continual alignment method for this task. It reconstructs an entity's representation based on entity adjacency, enabling it to generate embeddings for new entities quickly and inductively using their existing neighbors. It selects and replays partial pre-aligned entity pairs to train only parts of KGs while extracting trustworthy alignment for knowledge augmentation. As growing KGs inevitably contain non-matchable entities, different from previous works, the proposed method employs bidirectional nearest neighbor matching to find new entity alignment and update old alignment. Furthermore, we also construct new datasets by simulating the growth of multilingual DBpedia. Extensive experiments demonstrate that our continual alignment method is more effective than baselines based on retraining or inductive learning.
Enhancing Document-level Relation Extraction by Entity Knowledge Injection
Authors: Xinyi Wang, Zitao Wang, Weijian Sun, Wei Hu
Date: 2022-07-23 00:00:00
Tasks: Document-level Relation Extraction, Knowledge Graphs, Relation, Relation Extraction

Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document. It needs complex reasoning skills to synthesize various knowledge such as coreferences and commonsense. Large-scale knowledge graphs (KGs) contain a wealth of real-world facts, and can provide valuable knowledge to document-level RE. In this paper, we propose an entity knowledge injection framework to enhance current document-level RE models. Specifically, we introduce coreference distillation to inject coreference knowledge, endowing an RE model with the more general capability of coreference reasoning. We also employ representation reconciliation to inject factual knowledge and aggregate KG representations and document representations into a unified space. The experiments on two benchmark datasets validate the generalization of our entity knowledge injection framework and the consistent improvement to several document-level RE models.
Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks
Authors: Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Dilek Hakkani-Tur
Date: 2022-07-22 00:00:00
Proceeding: SIGDIAL (ACL) 2022 9
Tasks: Data Augmentation

While rich, open-domain textual data are generally available and may include interesting phenomena (humor, sarcasm, empathy, etc.) most are designed for language processing tasks, and are usually in a non-conversational format. In this work, we take a step towards automatically generating conversational data using Generative Conversational Networks, aiming to benefit from the breadth of available language and knowledge data, and train open domain social conversational agents. We evaluate our approach on conversations with and without knowledge on the Topical Chat dataset using automatic metrics and human evaluators. Our results show that for conversations without knowledge grounding, GCN can generalize from the seed data, producing novel conversations that are less relevant but more engaging and for knowledge-grounded conversations, it can produce more knowledge-focused, fluent, and engaging conversations. Specifically, we show that for open-domain conversations with 10\% of seed data, our approach performs close to the baseline that uses 100% of the data, while for knowledge-grounded conversations, it achieves the same using only 1% of the data, on human ratings of engagingness, fluency, and relevance.
Dimension of activity in random neural networks
Authors: David G. Clark, L. F. Abbott, Ashok Litwin-Kumar
Date: 2022-07-25 00:00:00

Neural networks are high-dimensional nonlinear dynamical systems that process information through the coordinated activity of many connected units. Understanding how biological and machine-learning networks function and learn requires knowledge of the structure of this coordinated activity, information contained, for example, in cross covariances between units. Self-consistent dynamical mean field theory (DMFT) has elucidated several features of random neural networks -- in particular, that they can generate chaotic activity -- however, a calculation of cross covariances using this approach has not been provided. Here, we calculate cross covariances self-consistently via a two-site cavity DMFT. We use this theory to probe spatiotemporal features of activity coordination in a classic random-network model with independent and identically distributed (i.i.d.) couplings, showing an extensive but fractionally low effective dimension of activity and a long population-level timescale. Our formulae apply to a wide range of single-unit dynamics and generalize to non-i.i.d. couplings. As an example of the latter, we analyze the case of partially symmetric couplings.
A Parallel Novelty Search Metaheuristic Applied to a Wildfire Prediction System
Authors: Jan Strappa, Paola Caymes-Scutari, Germán Bianchini
Date: 2022-07-24 00:00:00

Wildfires are a highly prevalent multi-causal environmental phenomenon. The impact of this phenomenon includes human losses, environmental damage and high economic costs. To mitigate these effects, several computer simulation systems have been developed in order to predict fire behavior based on a set of input parameters, also called a scenario (wind speed and direction; temperature; etc.). However, the results of a simulation usually have a high degree of error due to the uncertainty in the values of some variables, because they are not known, or because their measurement may be imprecise, erroneous, or impossible to perform in real time. Previous works have proposed the combination of multiple results in order to reduce this uncertainty. State-of-the-art methods are based on parallel optimization strategies that use a fitness function to guide the search among all possible scenarios. Although these methods have shown improvements in the quality of predictions, they have some limitations related to the algorithms used for the selection of scenarios. To overcome these limitations, in this work we propose to apply the Novelty Search paradigm, which replaces the objective function by a measure of the novelty of the solutions found, which allows the search to continuously generate solutions with behaviors that differ from one another. This approach avoids local optima and may be able to find useful solutions that would be difficult or impossible to find by other algorithms. As with existing methods, this proposal may also be adapted to other propagation models (floods, avalanches or landslides).
Unsupervised Hebbian Learning on Point Sets in StarCraft II
Authors: Beomseok Kang, Harshit Kumar, Saurabh Dash, Saibal Mukhopadhyay
Date: 2022-07-13 00:00:00
Tasks: Decoder, Self-Supervised Learning, Starcraft, Starcraft II

Learning the evolution of real-time strategy (RTS) game is a challenging problem in artificial intelligent (AI) system. In this paper, we present a novel Hebbian learning method to extract the global feature of point sets in StarCraft II game units, and its application to predict the movement of the points. Our model includes encoder, LSTM, and decoder, and we train the encoder with the unsupervised learning method. We introduce the concept of neuron activity aware learning combined with k-Winner-Takes-All. The optimal value of neuron activity is mathematically derived, and experiments support the effectiveness of the concept over the downstream task. Our Hebbian learning rule benefits the prediction with lower loss compared to self-supervised learning. Also, our model significantly saves the computational cost such as activations and FLOPs compared to a frame-based approach.
Contrastive Learning for Interactive Recommendation in Fashion
Authors: Karin Sevegnani, Arjun Seshadri, Tian Wang, Anurag Beniwal, Julian McAuley, Alan Lu, Gerard Medioni
Date: 2022-07-25 00:00:00
Tasks: Contrastive Learning, Interactive Recommendation, Recommendation Systems, Retrieval

Recommender systems and search are both indispensable in facilitating personalization and ease of browsing in online fashion platforms. However, the two tools often operate independently, failing to combine the strengths of recommender systems to accurately capture user tastes with search systems' ability to process user queries. We propose a novel remedy to this problem by automatically recommending personalized fashion items based on a user-provided text request. Our proposed model, WhisperLite, uses contrastive learning to capture user intent from natural language text and improves the recommendation quality of fashion products. WhisperLite combines the strength of CLIP embeddings with additional neural network layers for personalization, and is trained using a composite loss function based on binary cross entropy and contrastive loss. The model demonstrates a significant improvement in offline recommendation retrieval metrics when tested on a real-world dataset collected from an online retail fashion store, as well as widely used open-source datasets in different e-commerce domains, such as restaurants, movies and TV shows, clothing and shoe reviews. We additionally conduct a user study that captures user judgements on the relevance of the model's recommended items, confirming the relevancy of WhisperLite's recommendations in an online setting.