pwc-archive/papers-with-abstracts huggingface dataset:
A Minimal Computational Improviser Based on Oral Thought
Authors: Nick Montfort, Sebastian Bartlett Fernandez
Date: 2022-07-01 00:00:00
Proceeding: NAACL (Wordplay) 2022 7
Tasks: Cultural Vocal Bursts Intensity Prediction

A prototype system for playing a minimal improvisational game with one or more human or computer players is discussed. The game, Chain Reaction, has players collectively build a chain of word pairs or solid compounds. With a basis in oral culture, it emphasizes memory and rapid improvisation. Chains are only locally coherent, so absurdity and humor increases during play. While it is trivial to develop a computer player using textual corpora and literature-culture concepts, our approach is unique in that we have grounded our work in the principles of oral culture according to Walter Ong, an early scholar of orature. We show how a simple computer model can be designed to embody many aspects of oral poetics as theorized by Ong, suggesting design directions for other work in oral improvisation and poetics. The opportunities for own our system’s further development include creating culturally specific automated players and situating play in different temporal, physical, and social contexts.
Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code
Authors: Ryan Volum, Sudha Rao, Michael Xu, Gabriel DesGarennes, Chris Brockett, Benjamin Van Durme, Olivia Deng, Akanksha Malhotra, Bill Dolan
Date: 2022-07-01 00:00:00
Proceeding: NAACL (Wordplay) 2022 7
Tasks: Minecraft

Non-Player Characters (NPCs) significantly enhance the player experience in many games. Historically, players’ interactions with NPCs have tended to be highly scripted, to be limited to natural language responses to be selected by the player, and to not involve dynamic change in game state. In this work, we demonstrate that use of a few example conversational prompts can power a conversational agent to generate both natural language and novel code. This approach can permit development of NPCs with which players can have grounded conversations that are free-form and less repetitive. We demonstrate our approach using OpenAI Codex (GPT-3 finetuned on GitHub), with Minecraft game development as our test bed. We show that with a few example prompts, a Codex-based agent can generate novel code, hold multi-turn conversations and answer questions about structured data. We evaluate this application using experienced gamers in a Minecraft realm and provide analysis of failure cases and suggest possible directions for solutions.
A Sequence Modelling Approach to Question Answering in Text-Based Games
Authors: Gregory Furman, Edan Toledo, Jonathan Shock, Jan Buys
Date: 2022-07-01 00:00:00
Proceeding: NAACL (Wordplay) 2022 7
Tasks: Question Answering, text-based games

Interactive Question Answering (IQA) requires an intelligent agent to interact with a dynamic environment in order to gather information necessary to answer a question. IQA tasks have been proposed as means of training systems to develop language or visual comprehension abilities. To this end, the Question Answering with Interactive Text (QAit) task was created to produce and benchmark interactive agents capable of seeking information and answering questions in unseen environments. While prior work has exclusively focused on IQA as a reinforcement learning problem, such methods suffer from low sample efficiency and poor accuracy in zero-shot evaluation. In this paper, we propose the use of the recently proposed Decision Transformer architecture to provide improvements upon prior baselines. By utilising a causally masked GPT-2 Transformer for command generation and a BERT model for question answer prediction, we show that the Decision Transformer achieves performance greater than or equal to current state-of-the-art RL baselines on the QAit task in a sample efficient manner. In addition, these results are achievable by training on sub-optimal random trajectories, therefore not requiring the use of online agents to gather data.
Social Norms Guide Reference Resolution
Authors: Mitchell Abrams, Matthias Scheutz
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7

Humans use natural language, vision, and context to resolve referents in their environment. While some situated reference resolution is trivial, ambiguous cases arise when the language is underspecified or there are multiple candidate referents. This study investigates howpragmatic modulators external to the linguistic content are critical for the correct interpretation of referents in these scenarios. Inparticular, we demonstrate in a human subjects experiment how the social norms applicable in the given context influence theinterpretation of referring expressions. Additionally, we highlight how current coreference tools in natural language processing fail tohandle these ambiguous cases. We also briefly discuss the implications of this work for assistive robots which will routinely need to resolve referents in their environment.
Learning Natural Language Generation with Truncated Reinforcement Learning
Authors: Alice Martin, Guillaume Quispe, Charles Ollion, Sylvain Le Corff, Florian Strub, Olivier Pietquin
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Language Modeling, Language Modelling, Question Generation, Question-Generation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Text Generation

This paper introduces TRUncated ReinForcement Learning for Language (TrufLL), an original approach to train conditional languagemodels without a supervised learning phase, by only using reinforcement learning (RL). As RL methods unsuccessfully scale to large action spaces, we dynamically truncate the vocabulary space using a generic language model. TrufLL thus enables to train a language agent by solely interacting with its environment without any task-specific prior knowledge; it is only guided with a task-agnostic language model. Interestingly, this approach avoids the dependency to labelled datasets and inherently reduces pretrained policy flaws such as language or exposure biases. We evaluate TrufLL on two visual question generation tasks, for which we report positive results over performance and language metrics, which we then corroborate with a human evaluation. To our knowledge, it is the first approach that successfully learns a language generation policy without pre-training, using only reinforcement learning.
Language Model Augmented Monotonic Attention for Simultaneous Translation
Authors: Sathish Reddy Indurthi, Mohd Abbas Zaidi, Beomseok Lee, Nikhil Kumar Lakumarapu, Sangha Kim
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Language Modeling, Language Modelling, Machine Translation, model, Speech-to-Text, Speech-to-Text Translation, Translation

The state-of-the-art adaptive policies for Simultaneous Neural Machine Translation (SNMT) use monotonic attention to perform read/write decisions based on the partial source and target sequences. The lack of sufficient information might cause the monotonic attention to take poor read/write decisions, which in turn negatively affects the performance of the SNMT model. On the other hand, human translators make better read/write decisions since they can anticipate the immediate future words using linguistic information and domain knowledge. In this work, we propose a framework to aid monotonic attention with an external language model to improve its decisions. Experiments on MuST-C English-German and English-French speech-to-text translation tasks show the future information from the language model improves the state-of-the-art monotonic multi-head attention model further.
ErAConD: Error Annotated Conversational Dialog Dataset for Grammatical Error Correction
Authors: Xun Yuan, Derek Pham, Sam Davidson, Zhou Yu
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Chatbot, Grammatical Error Correction

Currently available grammatical error correction (GEC) datasets are compiled using essays or other long-form text written by language learners, limiting the applicability of these datasets to other domains such as informal writing and conversational dialog. In this paper, we present a novel GEC dataset consisting of parallel original and corrected utterances drawn from open-domain chatbot conversations; this dataset is, to our knowledge, the first GEC dataset targeted to a human-machine conversational setting. We also present a detailed annotation scheme which ranks errors by perceived impact on comprehension, making our dataset more representative of real-world language learning applications. To demonstrate the utility of the dataset, we use our annotated data to fine-tune a state-of-the-art GEC model. Experimental results show the effectiveness of our data in improving GEC model performance in a conversational scenario.
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification
Authors: S. K. Hong, Tae Young Jang
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Classification, Document Embedding, Few-Shot Learning, Few-Shot Text Classification, Meta-Learning, text-classification, Text Classification

Text classification has achieved great success with the prosperity of deep learning and pre-trained language models. However, we often encounter labeled data deficiency problems in real-world text-classification tasks. To overcome such challenging scenarios, interest in few-shot learning has increased, whereas most few-shot text classification studies suffer from a difficulty of utilizing pre-trained language models. In the study, we propose a novel learning method for learning how to attend, called LEA, through which meta-level attention aspects are derived based on our meta-learning strategy. This enables the generation of task-specific document embedding with leveraging pre-trained language models even though a few labeled data instances are given. We evaluate our proposed learning method on five benchmark datasets. The results show that the novel method robustly provides the competitive performance compared to recent few-shot learning methods for all the datasets.
Enhancing Self-Attention with Knowledge-Assisted Attention Maps
Authors: Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Defu Cao, Mingliang Zhang1, Yunhai Tong, Yaming Yang, Jing Bai, Ruofei Zhang, Hao Sun, Wei Shen
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Multi-Task Learning, Natural Language Understanding

Large-scale pre-trained language models have attracted extensive attentions in the research community and shown promising results on various tasks of natural language processing. However, the attention maps, which record the attention scores between tokens in self-attention mechanism, are sometimes ineffective as they are learned implicitly without the guidance of explicit semantic knowledge. Thus, we aim to infuse explicit external knowledge into pre-trained language models to further boost their performance. Existing works of knowledge infusion largely depend on multi-task learning frameworks, which are inefficient and require large-scale re-training when new knowledge is considered. In this paper, we propose a novel and generic solution, KAM-BERT, which directly incorporates knowledge-generated attention maps into the self-attention mechanism. It requires only a few extra parameters and supports efficient fine-tuning once new knowledge is added. KAM-BERT achieves consistent improvements on various academic datasets for natural language understanding. It also outperforms other state-of-the-art methods which conduct knowledge infusion into transformer-based architectures. Moreover, we apply our model to an industry-scale ad relevance application and show its advantages in the real-world scenario.
NewsEdits: A News Article Revision Dataset and a Novel Document-Level Reasoning Challenge
Authors: Alexander Spangher, Xiang Ren, Jonathan May, Nanyun Peng
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Articles

News article revision histories provide clues to narrative and factual evolution in news articles. To facilitate analysis of this evolution, we present the first publicly available dataset of news revision histories, NewsEdits. Our dataset is large-scale and multilingual; it contains 1.2 million articles with 4.6 million versions from over 22 English- and French-language newspaper sources based in three countries, spanning 15 years of coverage (2006-2021).We define article-level edit actions: Addition, Deletion, Edit and Refactor, and develop a high-accuracy extraction algorithm to identify these actions. To underscore the factual nature of many edit actions, we conduct analyses showing that added and deleted sentences are more likely to contain updating events, main content and quotes than unchanged sentences. Finally, to explore whether edit actions are predictable, we introduce three novel tasks aimed at predicting actions performed during version updates. We show that these tasks are possible for expert humans but are challenging for large NLP models. We hope this can spur research in narrative framing and help provide predictive tools for journalists chasing breaking news.
Do Deep Neural Nets Display Human-like Attention in Short Answer Scoring?
Authors: Zijie Zeng, Xinyu Li, Dragan Gasevic, Guanliang Chen
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7

Deep Learning (DL) techniques have been increasingly adopted for Automatic Text Scoring in education. However, these techniques often suffer from their inabilities to explain and justify how a prediction is made, which, unavoidably, decreases their trustworthiness and hinders educators from embracing them in practice. This study aimed to investigate whether (and to what extent) DL-based graders align with human graders regarding the important words they identify when marking short answer questions. To this end, we first conducted a user study to ask human graders to manually annotate important words in assessing answer quality and then measured the overlap between these human-annotated words and those identified by DL-based graders (i.e., those receiving large attention weights). Furthermore, we ran a randomized controlled experiment to explore the impact of highlighting important words detected by DL-based graders on human grading. The results showed that: (i) DL-based graders, to a certain degree, displayed alignment with human graders no matter whether DL-based graders and human graders agreed on the quality of an answer; and (ii) it is possible to facilitate human grading by highlighting those DL-detected important words, though further investigations are necessary to understand how human graders exploit such highlighted words.
Political Ideology and Polarization: A Multi-dimensional Approach
Authors: Barea Sinno, Bernardo Oviedo, Katherine Atwell, Malihe Alikhani, Junyi Jessy Li
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Articles, Stance Detection

Analyzing ideology and polarization is of critical importance in advancing our grasp of modern politics. Recent research has made great strides towards understanding the ideological bias (i.e., stance) of news media along the left-right spectrum. In this work, we instead take a novel and more nuanced approach for the study of ideology based on its left or right positions on the issue being discussed. Aligned with the theoretical accounts in political science, we treat ideology as a multi-dimensional construct, and introduce the first diachronic dataset of news articles whose ideological positions are annotated by trained political scientists and linguists at the paragraph level. We showcase that, by controlling for the author’s stance, our method allows for the quantitative and temporal measurement and analysis of polarization as a multidimensional ideological distance. We further present baseline models for ideology prediction, outlining a challenging task distinct from stance detection.
A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction
Authors: Shu Liu, Kaiwen Li, Zuhe Li
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Sentiment Triplet Extraction, Machine Reading Comprehension, Reading Comprehension, Sentiment Analysis, Triplet

Aspect sentiment triplet extraction (ASTE) is a challenging subtask in aspect-based sentiment analysis. It aims to explore the triplets of aspects, opinions and sentiments with complex correspondence from the context. The bidirectional machine reading comprehension (BMRC), can effectively deal with ASTE task, but several problems remains, such as query conflict and probability unilateral decrease. Therefore, this paper presents a robustly optimized BMRC method by incorporating four improvements. The word segmentation is applied to facilitate the semantic learning. Exclusive classifiers are designed to avoid the interference between different queries. A span matching rule is proposed to select the aspects and opinions that better represent the expectations of the model. The probability generation strategy is also introduced to obtain the predicted probability for aspects, opinions and aspect-opinion pairs. We have conducted extensive experiments on multiple benchmark datasets, where our model achieves the state-of-the-art performance.
SwahBERT: Language Model of Swahili
Authors: Gati Martin, Medard Edmund Mswahili, Young-Seob Jeong, Jeong Young-Seob
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Emotion Classification, Language Modeling, Language Modelling, model

The rapid development of social networks, electronic commerce, mobile Internet, and other technologies, has influenced the growth of Web data.Social media and Internet forums are valuable sources of citizens’ opinions, which can be analyzed for community development and user behavior analysis.Unfortunately, the scarcity of resources (i.e., datasets or language models) become a barrier to the development of natural language processing applications in low-resource languages.Thanks to the recent growth of online forums and news platforms of Swahili, we introduce two datasets of Swahili in this paper: a pre-training dataset of approximately 105MB with 16M words and annotated dataset of 13K instances for the emotion classification task.The emotion classification dataset is manually annotated by two native Swahili speakers.We pre-trained a new monolingual language model for Swahili, namely SwahBERT, using our collected pre-training data, and tested it with four downstream tasks including emotion classification.We found that SwahBERT outperforms multilingual BERT, a well-known existing language model, in almost all downstream tasks.
Empathic Machines: Using Intermediate Features as Levers to Emulate Emotions in Text-To-Speech Systems
Authors: Saiteja Kosgi, Sarath Sivaprasad, Niranjan Pedanekar, Anil Nelakanti, Vineet Gandhi
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: text-to-speech, Text to Speech

We present a method to control the emotional prosody of Text to Speech (TTS) systems by using phoneme-level intermediate features (pitch, energy, and duration) as levers. As a key idea, we propose Differential Scaling (DS) to disentangle features relating to affective prosody from those arising due to acoustics conditions and speaker identity. With thorough experimental studies, we show that the proposed method improves over the prior art in accurately emulating the desired emotions while retaining the naturalness of speech. We extend the traditional evaluation of using individual sentences for a more complete evaluation of HCI systems. We present a novel experimental setup by replacing an actor with a TTS system in offline and live conversations. The emotion to be rendered is either predicted or manually assigned. The results show that the proposed method is strongly preferred over the state-of-the-art TTS system and adds the much-coveted “human touch” in machine dialogue. Audio samples from our experiments and the code are available at: https://emtts.github.io/tts-demo/
The Why and The How: A Survey on Natural Language Interaction in Visualization
Authors: Henrik Voigt, Ozge Alacam, Monique Meuschke, Kai Lawonn, Sina Zarrieß
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7

Natural language as a modality of interaction is becoming increasingly popular in the field of visualization. In addition to the popular query interfaces, other language-based interactions such as annotations, recommendations, explanations, or documentation experience growing interest. In this survey, we provide an overview of natural language-based interaction in the research area of visualization. We discuss a renowned taxonomy of visualization tasks and classify 119 related works to illustrate the state-of-the-art of how current natural language interfaces support their performance. We examine applied NLP methods and discuss human-machine dialogue structures with a focus on initiative, duration, and communicative functions in recent visualization-oriented dialogue interfaces. Based on this overview, we point out interesting areas for the future application of NLP methods in the field of visualization.
Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding
Authors: Hao Huang, Xiubo Geng, Guodong Long, Daxin Jiang
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Reading Comprehension, Representation Learning

This work studies temporal reading comprehension (TRC), which reads a free-text passage and answers temporal ordering questions. Precise question understanding is critical for temporal reading comprehension. For example, the question “What happened before the victory” and “What happened after the victory” share almost all words except one, while their answers are totally different. Moreover, even if two questions query about similar temporal relations, different varieties might also lead to various answers. For example, although both the question “What usually happened during the press release?” and “What might happen during the press release” query events which happen after “the press release”, they convey divergent semantics.To this end, we propose a novel reading comprehension approach with precise question understanding. Specifically, a temporal ordering question is embedded into two vectors to capture the referred event and the temporal relation. Then we evaluate the temporal relation between candidate events and the referred event based on that. Such fine-grained representations offer two benefits. First, it enables a better understanding of the question by focusing on different elements of a question. Second, it provides good interpretability when evaluating temporal relations. Furthermore, we also harness an auxiliary contrastive loss for representation learning of temporal relations, which aims to distinguish relations with subtle but critical changes. The proposed approach outperforms strong baselines and achieves state-of-the-art performance on the TORQUE dataset. It also increases the accuracy of four pre-trained language models (BERT base, BERT large, RoBERTa base, and RoBETRa large), demonstrating its generic effectiveness on divergent models.
Ask Me Anything in Your Native Language
Authors: Nikita Sorokin, Dmitry Abulkhanov, Irina Piontkovskaya, Valentin Malykh
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Cross-Lingual Question Answering, Question Answering, Retrieval

Cross-lingual question answering is a thriving field in the modern world, helping people to search information on the web more efficiently. One of the important scenarios is to give an answer even there is no answer in the language a person asks a question with. We present a novel approach based on single encoder for query and passage for retrieval from multi-lingual collection, together with cross-lingual generative reader. It achieves a new state of the art in both retrieval and end-to-end tasks on the XOR TyDi dataset outperforming the previous results up to 10% on several languages. We find that our approach can be generalized to more than 20 languages in zero-shot approach and outperform all previous models by 12%.