Reviewerly/exHarmony huggingface dataset:
On Text Style Transfer via Style-Aware Masked Language Models

Text Style Transfer (TST) is performable through approaches such as latent space disentanglement, cycle-consistency losses, prototype editing etc. The prototype editing approach, which is known to be quite successful in TST, involves two key phases a) Masking of source style-associated tokens and b) Reconstruction of this source-style masked sentence conditioned with the target style. We follow a similar transduction method, in which we transpose the more difficult direct source to target TST task to a simpler Style-Masked Language Model (SMLM) Task, wherein, similar to BERT (CITATION), the goal of our model is now to reconstruct the source sentence from its style-masked version. We arrive at the SMLM mechanism naturally by formulating prototype editing/ transduction methods in a probabilistic framework, where TST resolves into estimating a hypothetical parallel dataset from a partially observed parallel dataset, wherein each domain is assumed to have a common latent style-masked prior. To generate this style-masked prior, we use “Explainable Attention” as our choice of attribution for a more precise style-masking step and also introduce a cost-effective and accurate “Attribution-Surplus” method of determining the position of masks from any arbitrary attribution model in O(1) time. We empirically show that this non-generational approach well suites the “content preserving” criteria for a task like TST, even for a complex style like Discourse Manipulation. Our model, the Style MLM, outperforms strong TST baselines and is on par with state-of-the-art TST models, which use complex architectures and orders of more parameters.
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text

Knowledge Graph (KG)-to-Text generation aims at generating fluent natural-language text that accurately represents the information of a given knowledge graph. While significant progress has been made in this task by exploiting the power of pre-trained language models (PLMs) with appropriate graph structure-aware modules, existing models still fall short of generating faithful text, especially when the ground-truth natural-language text contains additional information that is not present in the graph. In this paper, we develop a KG-to-text generation model that can generate faithful natural-language text from a given graph, in the presence of noisy reference text. Our framework incorporates two core ideas: Firstly, we utilize contrastive learning to enhance the model’s ability to differentiate between faithful and hallucinated information in the text, thereby encouraging the decoder to generate text that aligns with the input graph. Secondly, we empower the decoder to control the level of hallucination in the generated text by employing a controllable text generation technique. We evaluate our model’s performance through the standard quantitative metrics as well as a ChatGPT-based quantitative and qualitative analysis. Our evaluation demonstrates the superior performance of our model over state-of-the-art KG-to-text models on faithfulness.
ICM : Intent and Conversational Mining from Conversation Logs

Building conversation agents requires a large amount of manual effort in creating training data for intents / entities as well as mapping out extensive conversation flows. In this demonstration, we present ICM (Intent and conversation Mining), a tool which can be used to analyze existing conversation logs and help a bot designer analyze customer intents, train a custom intent model as well as map and optimize conversation flows. The tool can be used for first time deployment or subsequent deployments of chatbots.
Claim Optimization in Computational Argumentation

An optimal delivery of arguments is key to persuasion in any debate, both for humans and for AI systems. This requires the use of clear and fluent claims relevant to the given debate. Prior work has studied the automatic assessment of argument quality extensively. Yet, no approach actually improves the quality so far. To fill this gap, this paper proposes the task of claim optimization: to rewrite argumentative claims in order to optimize their delivery. As multiple types of optimization are possible, we approach this task by first generating a diverse set of candidate claims using a large language model, such as BART, taking into account contextual information. Then, the best candidate is selected using various quality metrics. In automatic and human evaluation on an English-language corpus, our quality-based candidate selection outperforms several baselines, improving 60% of all claims (worsening 16% only). Follow-up analyses reveal that, beyond copy editing, our approach often specifies claims with details, whereas it adds less evidence than humans do. Moreover, its capabilities generalize well to other domains, such as instructional texts.
Towards Learning Action Models from Narrative Text Through Extraction and Ordering of Structured Events

Event models, in the form of scripts, frames, or precondition/effect axioms, allow for reasoning about the causal and motivational connections between events in a story, and thus are central to AI understanding and generating narratives. However, previous efforts to learn general structured event models from text have overlooked important challenges raised by the narrative text, such as the complex (nested) event arguments and inferring the order and actuality of mentioned events. We present an NLP pipeline for extracting (partially) ordered, structured event representations for use in learning general event models from three large text corpora. We address each of the challenges that we identify to some degree, but also conclude that they raise open problems for future research.
Reports, Observations, and Belief Change

We consider belief change in a context where information comes from reports, and the reporting agents may not be honest. In order to capture this process, we introduce an extended class of epistemic states that includes a history of past reports received. We present a set of postulates that describe how new reports should be incorporated. The postulates describe a new kind of belief change operator, where reported information can either be believed or ignored. We then provide a representation result for these postulates, which characterizes report revision in terms of an underlying set of agents that are perceived to be honest. We then extend our framework by adding observations. In this framework, observations are understood to be highly reliable. As such, when an observation conflicts with a report, we must question the honesty of the agent that provided the report. We introduce a flexible framework where we can set a threshold for the number of false reports an agent can send before they are labelled dishonest. Fundamental results are provided, along with a discussion on key future problems to be addressed in trust and belief revision.
Pelatihan Pembuatan Media Pembelajaran Interaktif Berbasis Kahoot Bagi Pendidik di SMAS Islam Ibadurrahman

This training activity is motivated by the challenges faced by our partners, namely that most educators primarily use traditional teaching methods, relying on chalkboards and occasionally incorporating PowerPoint slides. A significant number of educators employ lecture-style teaching during the learning process, resulting in a classroom environment that becomes monotonous and causes students to lose focus. Consequently, learners are unable to concentrate on the lessons being delivered in the classroom. Therefore, there is a pressing need for engaging learning media that meet the needs of students and enhance their learning determination. This training program is specifically designed for educators at SMAS Islam Ibadurrahman, as many educators there encounter difficulties in applying various media or applications in teaching, especially in the field of mathematics. The objective of this training is to enhance the educators' ability to utilize existing technology, particularly the Kahoot platform, as a means of teaching. The aim is to make educators more creative and capable of increasing students' interest in learning. The training activities was focus on the used and creation of Kahoot-based learning media. The outcome of this initiative was expected to increased the knowledge and skills of educators at SMAS Islam Ibadurrahman regarding the use of Kahoot as a teaching tool in the classroom.
Research on the relationship between achievement motivation and individual emotional state: the promoting effect of positive emotions

Artificial intelligence is showing increasing potential, changing our way of life and work. However, with the further development of artificial intelligence technology, we also need to consider the potential risks and challenges brought by these technologies. This article introduces some of the main issues faced by artificial intelligence, including data privacy and security, inequality and discrimination, employment and economic impacts, ethical and moral issues, etc. To address these issues, we need to establish corresponding legal and policy frameworks and maintain regulation and control over artificial intelligence technology. At the same time, we also need to invest in the research and development of artificial intelligence, cultivate relevant talents, and promote the sustainable development of artificial intelligence. Only in this way can artificial intelligence better bring benefits to our society and economy.
Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Ranking

Dialogue systems need to produce responses that realize multiple types of dialogue acts (DAs) with high semantic fidelity. In the past, natural language generators (NLGs) for dialogue were trained on large parallel corpora that map from a domain-specific DA and its semantic attributes to an output utterance. Recent work shows that pretrained language models (LLMs) offer new possibilities for controllable NLG using prompt-based learning. Here we develop a novel few-shot overgenerate-and-rank approach that achieves the controlled generation of DAs. We compare eight few-shot prompt styles that include a novel method of generating from textual pseudo-references using a textual style transfer approach. We develop six automatic ranking functions that identify outputs with both the correct DA and high semantic accuracy at generation time. We test our approach on three domains and four LLMs. To our knowledge, this is the first work on NLG for dialogue that automatically ranks outputs using both DA and attribute accuracy. For completeness, we compare our results to fine-tuned few-shot models trained with 5 to 100 instances per DA. Our results show that several prompt settings achieve perfect DA accuracy, and near perfect semantic accuracy (99.81%) and perform better than few-shot fine-tuning.
Transformer-based Multi-Party Conversation Generation using Dialogue Discourse Acts Planning

Recent transformer-based approaches to multi-party conversation generation may produce syntactically coherent but discursively inconsistent dialogues in some cases. To address this issue, we propose an approach to integrate a dialogue act planning stage into the end-to-end transformer-based generation pipeline. This approach consists of a transformer fine-tuning procedure based on linearized dialogue representations that include special discourse tokens. The obtained results demonstrate that incorporating discourse tokens into training sequences is sufficient to significantly improve dialogue consistency and overall generation quality. The suggested approach performs well, including for automatically annotated data. Apart from that, it is observed that increasing the weight of the discourse planning task in the loss function accelerates learning convergence.
Towards Breaking the Self-imposed Filter Bubble in Argumentative Dialogues

Human users tend to selectively ignore information that contradicts their pre-existing beliefs or opinions in their process of information seeking. These “self-imposed filter bubbles” (SFB) pose a significant challenge for cooperative argumentative dialogue systems aiming to build an unbiased opinion and a better understanding of the topic at hand. To address this issue, we develop a strategy for overcoming users’ SFB within the course of the interaction. By continuously modeling the user’s position in relation to the SFB, we are able to identify the respective arguments which maximize the probability to get outside the SFB and present them to the user. We implemented this approach in an argumentative dialogue system and evaluated in a laboratory user study with 60 participants to show its validity and applicability. The findings suggest that the strategy was successful in breaking users’ SFBs and promoting a more reflective and comprehensive discussion of the topic.
Unravelling Indirect Answers to Wh-Questions: Corpus Construction, Analysis, and Generation

Indirect answers, crucial in human communication, serve to maintain politeness, avoid conflicts, and align with social customs. Although there has been a substantial number of studies on recognizing and understanding indirect answers to polar questions (often known as yes/no questions), there is a dearth of such work regarding wh-questions. This study takes up the challenge by constructing what is, to our knowledge, the first corpus of indirect answers to wh-questions. We analyze and interpret indirect answers to different wh-questions based on our carefully compiled corpus. In addition, we conducted a pilot study on generating indirect answers to wh-questions by fine-tuning the pre-trained generative language model DialoGPT (Zhang et al., 2020). Our results suggest this is a task that GPT finds difficult.
What’s Hard in English RST Parsing? Predictive Models for Error Analysis

Despite recent advances in Natural Language Processing (NLP), hierarchical discourse parsing in the framework of Rhetorical Structure Theory remains challenging, and our understanding of the reasons for this are as yet limited. In this paper, we examine and model some of the factors associated with parsing difficulties in previous work: the existence of implicit discourse relations, challenges in identifying long-distance relations, out-of-vocabulary items, and more. In order to assess the relative importance of these variables, we also release two annotated English test-sets with explicit correct and distracting discourse markers associated with gold standard RST relations. Our results show that as in shallow discourse parsing, the explicit/implicit distinction plays a role, but that long-distance dependencies are the main challenge, while lack of lexical overlap is less of a problem, at least for in-domain parsing. Our final model is able to predict where errors will occur with an accuracy of 76.3% for the bottom-up parser and 76.6% for the top-down parser.
Construction and Sustainable Development of Ecological English Classroom Based on Internet Technology

The ecological English classroom is in the form of English teaching, which aims to give students the idea of ecological and environmental friendliness. The ecological English classroom has various teaching forms, which can increase the interest in English learning and integrate the awareness of protecting the ecology and the environment into the learning. This paper aims to use Internet technology to build a more intuitive and effective ecological English classroom. For the construction of ecological English classroom, this paper is based on Internet technology and uses cloud data centers to integrate and transmit ecological resources. This paper is divided into three parts, and it evaluates the students' achievements in three aspects: cooperation and communication ability, problem-solving ability and information literacy. In the experiment, this paper selects two grades of students for testing. The experimental group was taught in the ecological English classroom constructed in this paper, and the control group was taught by the traditional English curriculum. The experimental period was three months. Comparing the scores of the pre-test and post-test, it can be found that the scores of the experimental group and the control group both decreased by 5%. The problem solving ability and information literacy ability of the students in the experimental group increased by 20% and 34% respectively, which were 16% and 36% higher than that of the control group. This shows that the ecological English classroom constructed in this paper can effectively allow students to master the relevant knowledge of ecological, and their own awareness of ecological environmental protection is also continuously strengthened.
Dialog Action-Aware Transformer for Dialog Policy Learning

Recent works usually address Dialog policy learning DPL by training a reinforcement learning (RL) agent to determine the best dialog action. However, existing works on deep RL require a large volume of agent-user interactions to achieve acceptable performance. In this paper, we propose to make full use of the plain text knowledge from the pre-trained language model to accelerate the RL agent’s learning speed. Specifically, we design a dialog action-aware transformer encoder (DaTrans), which integrates a new fine-tuning procedure named masked last action task to encourage DaTrans to be dialog-aware and distill action-specific features. Then, DaTrans is further optimized in an RL setting with ongoing interactions and evolves through exploration in the dialog action space toward maximizing long-term accumulated rewards. The effectiveness and efficiency of the proposed model are demonstrated with both simulator evaluation and human evaluation.
Intelligent Platform for Educational Resources of Computer Basic Courses in the Digital Education Environment

Computer science is a highly practical professional foundation course, and its teaching purpose is to equip students with basic computer application skills and prepare them for future work. Due to the lack of clear goals in current computer foundation courses, teachers mainly adopt an "indoctrination" teaching method that only emphasizes theory and not practice, resulting in students becoming accustomed to "contact based" learning without applying theory to practice. Therefore, it is necessary to choose a scientific and reasonable teaching strategy based on the specific purpose of computer teaching and students' actual mastery, so that students can proficiently master and apply this knowledge in practice. On this basis, this article first described the main problems in the teaching of computer basic courses and the impact of digital learning environment on contemporary education models, thereby highlighting the necessity of building a computer basic course resource platform. After that, this article discussed a cloud service platform for computer basic course education resources. Finally, through experimental analysis and survey questionnaires, it was proven that the response time of the designed system was shorter than that of traditional systems; the accuracy of the system was superior to traditional systems, and 66.23% of respondents were satisfied with the system.
PaperPersiChat: Scientific Paper Discussion Chatbot using Transformers and Discourse Flow Management

The rate of scientific publications is increasing exponentially, necessitating a significant investment of time in order to read and comprehend the most important articles. While ancillary services exist to facilitate this process, they are typically closed-model and paid services or have limited capabilities. In this paper, we present PaperPersiChat, an open chatbot-system designed for the discussion of scientific papers. This system supports summarization and question-answering modes within a single end-to-end chatbot pipeline, which is guided by discourse analysis. To expedite the development of similar systems, we also release the gathered dataset, which has no publicly available analogues.
Question Generation to Elicit Users’ Food Preferences by Considering the Semantic Content

To obtain a better understanding of user preferences in providing tailored services, dialogue systems have to generate semi-structured interviews that require flexible dialogue control while following a topic guide to accomplish the purpose of the interview. Toward this goal, this study proposes a semantics-aware GPT-3 fine-tuning model that generates interviews to acquire users’ food preferences. The model was trained using dialogue history and semantic representation constructed from the communicative function and semantic content of the utterance. Using two baseline models: zero-shot ChatGPT and fine-tuned GPT-3, we conducted a user study for subjective evaluations alongside automatic objective evaluations. In the user study, in impression rating, the outputs of the proposed model were superior to those of baseline models and comparable to real human interviews in terms of eliciting the interviewees’ food preferences.
The Road to Quality is Paved with Good Revisions: A Detailed Evaluation Methodology for Revision Policies in Incremental Sequence Labelling

Incremental dialogue model components produce a sequence of output prefixes based on incoming input. Mistakes can occur due to local ambiguities or to wrong hypotheses, making the ability to revise past outputs a desirable property that can be governed by a policy. In this work, we formalise and characterise edits and revisions in incremental sequence labelling and propose metrics to evaluate revision policies. We then apply our methodology to profile the incremental behaviour of three Transformer-based encoders in various tasks, paving the road for better revision policies.
Resolving References in Visually-Grounded Dialogue via Text Generation

Vision-language models (VLMs) have shown to be effective at image retrieval based on simple text queries, but text-image retrieval based on conversational input remains a challenge. Consequently, if we want to use VLMs for reference resolution in visually-grounded dialogue, the discourse processing capabilities of these models need to be augmented. To address this issue, we propose fine-tuning a causal large language model (LLM) to generate definite descriptions that summarize coreferential information found in the linguistic context of references. We then use a pretrained VLM to identify referents based on the generated descriptions, zero-shot. We evaluate our approach on a manually annotated dataset of visually-grounded dialogues and achieve results that, on average, exceed the performance of the baselines we compare against. Furthermore, we find that using referent descriptions based on larger context windows has the potential to yield higher returns.
No that’s not what I meant: Handling Third Position Repair in Conversational Question Answering

The ability to handle miscommunication is crucial to robust and faithful conversational AI. People usually deal with miscommunication immediately as they detect it, using highly systematic interactional mechanisms called repair. One important type of repair is Third Position Repair (TPR) whereby a speaker is initially misunderstood but then corrects the misunderstanding as it becomes apparent after the addressee’s erroneous response. Here, we collect and publicly release REPAIR-QA, the first large dataset of TPRs in a conversational question answering (QA) setting. The data is comprised of the TPR turns, corresponding dialogue contexts, and candidate repairs of the original turn for execution of TPRs. We demonstrate the usefulness of the data by training and evaluating strong baseline models for executing TPRs. For stand-alone TPR execution, we perform both automatic and human evaluations on a fine-tuned T5 model, as well as OpenAI’s GPT-3 LLMs. Additionally, we extrinsically evaluate the LLMs’ TPR processing capabilities in the downstream conversational QA task. The results indicate poor out-of-the-box performance on TPR’s by the GPT-3 models, which then significantly improves when exposed to REPAIR-QA.