pwc-archive/papers-with-abstracts huggingface dataset:
UOR at SemEval-2021 Task 12: On Crowd Annotations; Learning with Disagreements to optimise crowd truth
Authors: Emmanuel Osei-Brefo, Thanet Markchom, HuiZhi Liang
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021

Crowdsourcing has been ubiquitously used for annotating enormous collections of data. However, the major obstacles to using crowd-sourced labels are noise and errors from non-expert annotations. In this work, two approaches dealing with the noise and errors in crowd-sourced labels are proposed. The first approach uses Sharpness-Aware Minimization (SAM), an optimization technique robust to noisy labels. The other approach leverages a neural network layer called softmax-Crowdlayer specifically designed to learn from crowd-sourced annotations. According to the results, the proposed approaches can improve the performance of the Wide Residual Network model and Multi-layer Perception model applied on crowd-sourced datasets in the image processing domain. It also has similar and comparable results with the majority voting technique when applied to the sequential data domain whereby the Bidirectional Encoder Representations from Transformers (BERT) is used as the base model in both instances.
Towards Multi-Modal Text-Image Retrieval to improve Human Reading
Authors: Florian Schneider, {\"O}zge Ala{\c{c}}am, Xintong Wang, Chris Biemann
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Image Retrieval, Reading Comprehension, Retrieval

In primary school, children{'}s books, as well as in modern language learning apps, multi-modal learning strategies like illustrations of terms and phrases are used to support reading comprehension. Also, several studies in educational psychology suggest that integrating cross-modal information will improve reading comprehension. We claim that state-of- he-art multi-modal transformers, which could be used in a language learner context to improve human reading, will perform poorly because of the short and relatively simple textual data those models are trained with. To prove our hypotheses, we collected a new multi-modal image-retrieval dataset based on data from Wikipedia. In an in-depth data analysis, we highlight the differences between our dataset and other popular datasets. Additionally, we evaluate several state-of-the-art multi-modal transformers on text-image retrieval on our dataset and analyze their meager results, which verify our claims.
Advances in Debating Technologies: Building AI That Can Debate Humans
Authors: Roy Bar-Haim, Liat Ein-Dor, Matan Orbach, Elad Venezian, Noam Slonim
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Argument Mining, Stance Classification

The tutorial focuses on Debating Technologies, a sub-field of computational argumentation defined as {``}computational technologies developed directly to enhance, support, and engage with human debating{''} (Gurevych et al., 2016). A recent milestone in this field is Project Debater, which was revealed in 2019 as the first AI system that can debate human experts on complex topics. Project Debater is the third in the series of IBM Research AI{'}s grand challenges, following Deep Blue and Watson. It has been developed for over six years by a large team of researchers and engineers, and its live demonstration in February 2019 received massive media attention. This research effort has resulted in more than 50 scientific papers to date, and many datasets freely available for research purposes. We discuss the scientific challenges that arise when building such a system, including argument mining, argument quality assessment, stance classification, principled argument detection, narrative generation, and rebutting a human opponent. Many of the underlying capabilities of Project Debater have been made freely available for academic research, and the tutorial will include a detailed explanation of how to use and leverage these tools. In addition to discussing individual components, the tutorial also provides a holistic view of a debating system. Such a view is largely missing in the academic literature, where each paper typically addresses a specific problem in isolation. We present a complete pipeline of a debating system, and discuss the information flow and the interaction between the various components. Finally, we discuss practical applications and future challenges of debating technologies.
Event-Centric Natural Language Processing
Authors: Muhao Chen, Hongming Zhang, Qiang Ning, Manling Li, Heng Ji, Kathleen McKeown, Dan Roth
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5

This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text. These include methods to extract the internal structures of an event regarding its protagonist(s), participant(s) and properties, as well as external structures concerning memberships, temporal and causal relations of multiple events. This tutorial will provide audience with a systematic introduction of (i) knowledge representations of events, (ii) various methods for automated extraction, conceptualization and prediction of events and their relations, (iii) induction of event processes and properties, and (iv) a wide range of NLU and commonsense understanding tasks that benefit from aforementioned techniques. We will conclude the tutorial by outlining emerging research problems in this area.
Meta Learning and Its Applications to Natural Language Processing
Authors: Hung-Yi Lee, Ngoc Thang Vu, Shang-Wen Li
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Dialogue Generation, Few-Shot Learning, Machine Translation, Meta-Learning, Relation Extraction

Deep learning based natural language processing (NLP) has become the mainstream of research in recent years and significantly outperforms conventional methods. However, deep learning models are notorious for being data and computation hungry. These downsides limit the application of such models from deployment to different domains, languages, countries, or styles, since collecting in-genre data and model training from scratch are costly. The long-tail nature of human language makes challenges even more significant. Meta-learning, or {`}Learning to Learn{'}, aims to learn better learning algorithms, including better parameter initialization, optimization strategy, network architecture, distance metrics, and beyond. Meta-learning has been shown to allow faster fine-tuning, converge to better performance, and achieve amazing results for few-shot learning in many applications. Meta-learning is one of the most important new techniques in machine learning in recent years. There is a related tutorial in ICML 2019 and a related course at Stanford, but most of the example applications given in these materials are about image processing. It is believed that meta-learning has great potential to be applied in NLP, and some works have been proposed with notable achievements in several relevant problems, e.g., relation extraction, machine translation, and dialogue generation and state tracking. However, it does not catch the same level of attention as in the image processing community. In the tutorial, we will first introduce Meta-learning approaches and the theory behind them, and then review the works of applying this technology to NLP problems. This tutorial intends to facilitate researchers in the NLP community to understand this new technology better and promote more research studies using this new technology.
Pre-training Methods for Neural Machine Translation
Authors: Mingxuan Wang, Lei LI
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Machine Translation, NMT, Translation

This tutorial provides a comprehensive guide to make the most of pre-training for neural machine translation. Firstly, we will briefly introduce the background of NMT, pre-training methodology, and point out the main challenges when applying pre-training for NMT. Then we will focus on analysing the role of pre-training in enhancing the performance of NMT, how to design a better pre-training model for executing specific NMT tasks and how to better integrate the pre-trained model into NMT system. In each part, we will provide examples, discuss training techniques and analyse what is transferred when applying pre-training.
Prosody: Models, Methods, and Applications
Authors: Nigel Ward, Gina-Anne Levow
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5

Prosody is essential in human interaction, enabling people to show interest, establish rapport, efficiently convey nuances of attitude or intent, and so on. Some applications that exploit prosodic knowledge have recently shown superhuman performance, and in many respects our ability to effectively model prosody is rapidly advancing. This tutorial will overview the computational modeling of prosody, including recent advances and diverse actual and potential applications.
Recognizing Multimodal Entailment
Authors: Cesar Ilharco, Afsaneh Shirazi, Arjun Gopalan, Arsha Nagrani, Blaz Bratanic, Chris Bregler, Christina Funk, Felipe Ferreira, Gabriel Barcik, Gabriel Ilharco, Georg Osang, Jannis Bulian, Jared Frank, Lucas Smaira, Qin Cao, Ricardo Marino, Roma Patel, Thomas Leung, Vaiva Imbrasaite
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Graph Learning, Question Answering

How information is created, shared and consumed has changed rapidly in recent decades, in part thanks to new social platforms and technologies on the web. With ever-larger amounts of unstructured and limited labels, organizing and reconciling information from different sources and modalities is a central challenge in machine learning. This cutting-edge tutorial aims to introduce the multimodal entailment task, which can be useful for detecting semantic alignments when a single modality alone does not suffice for a whole content understanding. Starting with a brief overview of natural language processing, computer vision, structured data and neural graph learning, we lay the foundations for the multimodal sections to follow. We then discuss recent multimodal learning literature covering visual, audio and language streams, and explore case studies focusing on tasks which require fine-grained understanding of visual and linguistic semantics question answering, veracity and hatred classification. Finally, we introduce a new dataset for recognizing multimodal entailment, exploring it in a hands-on collaborative section. Overall, this tutorial gives an overview of multimodal learning, introduces a multimodal entailment dataset, and encourages future research in the topic.
Transformer-Based Direct Hidden Markov Model for Machine Translation
Authors: Weiyue Wang, Zijian Yang, Yingbo Gao, Hermann Ney
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Machine Translation, Translation

The neural hidden Markov model has been proposed as an alternative to attention mechanism in machine translation with recurrent neural networks. However, since the introduction of the transformer models, its performance has been surpassed. This work proposes to introduce the concept of the hidden Markov model to the transformer architecture, which outperforms the transformer baseline. Interestingly, we find that the zero-order model already provides promising performance, giving it an edge compared to a model with first-order dependency, which performs similarly but is significantly slower in training and decoding.
On the Relationship between Zipf's Law of Abbreviation and Interfering Noise in Emergent Languages
Authors: Ryo Ueda, Koki Washio
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5

This paper studies whether emergent languages in a signaling game follow Zipf{'}s law of abbreviation (ZLA), especially when the communication ability of agents is limited because of interfering noises. ZLA is a well-known tendency in human languages where the more frequently a word is used, the shorter it will be. Surprisingly, previous work demonstrated that emergent languages do not obey ZLA at all when neural agents play a signaling game. It also reported that a ZLA-like tendency appeared by adding an explicit penalty on word lengths, which can be considered some external factors in reality such as articulatory effort. We hypothesize, on the other hand, that there might be not only such external factors but also some internal factors related to cognitive abilities. We assume that it could be simulated by modeling the effect of noises on the agents{'} environment. In our experimental setup, the hidden states of the LSTM-based speaker and listener were added with Gaussian noise, while the channel was subject to discrete random replacement. Our results suggest that noise on a speaker is one of the factors for ZLA or at least causes emergent languages to approach ZLA, while noise on a listener and a channel is not.
Attending Self-Attention: A Case Study of Visually Grounded Supervision in Vision-and-Language Transformers
Authors: Jules Samaran, Noa Garcia, Mayu Otani, Chenhui Chu, Yuta Nakashima
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Language Modeling, Language Modelling, Visual Grounding

The impressive performances of pre-trained visually grounded language models have motivated a growing body of research investigating what has been learned during the pre-training. As a lot of these models are based on Transformers, several studies on the attention mechanisms used by the models to learn to associate phrases with their visual grounding in the image have been conducted. In this work, we investigate how supervising attention directly to learn visual grounding can affect the behavior of such models. We compare three different methods on attention supervision and their impact on the performances of a state-of-the-art visually grounded language model on two popular vision-and-language tasks.
Video-guided Machine Translation with Spatial Hierarchical Attention Network
Authors: Weiqi Gu, Haiyue Song, Chenhui Chu, Sadao Kurohashi
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Action Detection, Machine Translation, Translation, Video-Guided Machine Translation

Video-guided machine translation, as one type of multimodal machine translations, aims to engage video contents as auxiliary information to address the word sense ambiguity problem in machine translation. Previous studies only use features from pretrained action detection models as motion representations of the video to solve the verb sense ambiguity, leaving the noun sense ambiguity a problem. To address this problem, we propose a video-guided machine translation system by using both spatial and motion representations in videos. For spatial features, we propose a hierarchical attention network to model the spatial information from object-level to video-level. Experiments on the VATEX dataset show that our system achieves 35.86 BLEU-4 score, which is 0.51 score higher than the single model of the SOTA method.
Stylistic approaches to predicting Reddit popularity in diglossia
Authors: Huikai Chua
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5

Past work investigating what makes a Reddit post popular has indicated that style is a far better predictor than content, where posts conforming to a subreddit{'}s community style are better received. However, what about a diglossia, when there are two community styles? In Singapore, the basilect ({`}Singlish{'}) co-exists with an acrolect (standard English), each with contrasting advantages of community identity and prestige respectively. In this paper, I apply stylistic approaches to predicting Reddit post scores in a diglossia. Using data from the Singaporean and British subreddits, I show that while the acrolect{'}s prestige attracts more upvotes, the most popular posts also draw on Singlish vocabulary to appeal to the community identity.
``I've Seen Things You People Wouldn't Believe'': Hallucinating Entities in GuessWhat?!
Authors: Alberto Testoni, Raffaella Bernardi
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Hallucination, Image Captioning, Object Hallucination, Text Generation

Natural language generation systems have witnessed important progress in the last years, but they are shown to generate tokens that are unrelated to the source input. This problem affects computational models in many NLP tasks, and it is particularly unpleasant in multimodal systems. In this work, we assess the rate of object hallucination in multimodal conversational agents playing the GuessWhat?! referential game. Better visual processing has been shown to mitigate this issue in image captioning; hence, we adapt to the GuessWhat?! task the best visual processing models at disposal, and propose two new models to play the Questioner agent. We show that the new models generate few hallucinations compared to other renowned models available in the literature. Moreover, their hallucinations are less severe (affect task-accuracy less) and are more human-like. We also analyse where hallucinations tend to occur more often through the dialogue: hallucinations are less frequent in earlier turns, cause a cascade hallucination effect, and are often preceded by negative answers, which have been shown to be harder to ground.
How do different factors Impact the Inter-language Similarity? A Case Study on Indian languages
Authors: Sourav Kumar, Salil Aggarwal, Dipti Misra Sharma, Radhika Mamidi
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Machine Translation, Sentiment Analysis, Translation

India is one of the most linguistically diverse nations of the world and is culturally very rich. Most of these languages are somewhat similar to each other on account of sharing a common ancestry or being in contact for a long period of time. Nowadays, researchers are constantly putting efforts in utilizing the language relatedness to improve the performance of various NLP systems such as cross lingual semantic search, machine translation, sentiment analysis systems, etc. So in this paper, we performed an extensive case study on similarity involving languages of the Indian subcontinent. Language similarity prediction is defined as the task of measuring how similar the two languages are on the basis of their lexical, morphological and syntactic features. In this study, we concentrate only on the approach to calculate lexical similarity between Indian languages by looking at various factors such as size and type of corpus, similarity algorithms, subword segmentation, etc. The main takeaways from our work are: (i) Relative order of the language similarities largely remain the same, regardless of the factors mentioned above, (ii) Similarity within the same language family is higher, (iii) Languages share more lexical features at the subword level.
COVID-19 and Misinformation: A Large-Scale Lexical Analysis on Twitter
Authors: Dimosthenis Antypas, Jose Camacho-Collados, Alun Preece, David Rogers
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Lexical Analysis, Misinformation

Social media is often used by individuals and organisations as a platform to spread misinformation. With the recent coronavirus pandemic we have seen a surge of misinformation on Twitter, posing a danger to public health. In this paper, we compile a large COVID-19 Twitter misinformation corpus and perform an analysis to discover patterns with respect to vocabulary usage. Among others, our analysis reveals that the variety of topics and vocabulary usage are considerably more limited and negative in tweets related to misinformation than in randomly extracted tweets. In addition to our qualitative analysis, our experimental results show that a simple linear model based only on lexical features is effective in identifying misinformation-related tweets (with accuracy over 80{\%}), providing evidence to the fact that the vocabulary used in misinformation largely differs from generic tweets.
Situation-Based Multiparticipant Chat Summarization: a Concept, an Exploration-Annotation Tool and an Example Collection
Authors: Anna Smirnova, Evgeniy Slobodkin, George Chernishev
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Information Retrieval, Retrieval

Currently, text chatting is one of the primary means of communication. However, modern text chat still in general does not offer any navigation or even full-featured search, although the high volumes of messages demand it. In order to mitigate these inconveniences, we formulate the problem of situation-based summarization and propose a special data annotation tool intended for developing training and gold-standard data. A situation is a subset of messages revolving around a single event in both temporal and contextual senses: e.g, a group of friends arranging a meeting in chat, agreeing on date, time, and place. Situations can be extracted via information retrieval, natural language processing, and machine learning techniques. Since the task is novel, neither training nor gold-standard datasets for it have been created yet. In this paper, we present the formulation of the situation-based summarization problem. Next, we describe Chat Corpora Annotator (CCA): the first annotation system designed specifically for exploring and annotating chat log data. We also introduce a custom query language for semi-automatic situation extraction. Finally, we present the first gold-standard dataset for situation-based summarization. The software source code and the dataset are publicly available.
Modeling Text using the Continuous Space Topic Model with Pre-Trained Word Embeddings
Authors: Seiichi Inoue, Taichi Aida, Mamoru Komachi, Manabu Asai
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Document Classification, Word Embeddings

In this study, we propose a model that extends the continuous space topic model (CSTM), which flexibly controls word probability in a document, using pre-trained word embeddings. To develop the proposed model, we pre-train word embeddings, which capture the semantics of words and plug them into the CSTM. Intrinsic experimental results show that the proposed model exhibits a superior performance over the CSTM in terms of perplexity and convergence speed. Furthermore, extrinsic experimental results show that the proposed model is useful for a document classification task when compared with the baseline model. We qualitatively show that the latent coordinates obtained by training the proposed model are better than those of the baseline model.
Joint Detection and Coreference Resolution of Entities and Events with Document-level Context Aggregation
Authors: Samuel Kriman, Heng Ji
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: coreference-resolution, Coreference Resolution, Knowledge Graphs, Sentence

Constructing knowledge graphs from unstructured text is an important task that is relevant to many domains. Most previous work focuses on extracting information from sentences or paragraphs, due to the difficulty of analyzing longer contexts. In this paper we propose a new jointly trained model that can be used for various information extraction tasks at the document level. The tasks performed by this system are entity and event identification, typing, and coreference resolution. In order to improve entity and event typing, we utilize context-aware representations aggregated from the detected mentions of the corresponding entities and events across the entire document. By extending our system to document-level, we can improve our results by incorporating cross-sentence dependencies and additional contextual information that might not be available at the sentence level, which allows for more globally optimized predictions. We evaluate our system on documents from the ACE05-E+ dataset and find significant improvement over the sentence-level SOTA on entity and event trigger identification and classification.