Reviewerly/exHarmony huggingface dataset:
Off-Policy Deep Reinforcement Learning with Analogous Disentangled  Exploration

Off-policy reinforcement learning (RL) is concerned with learning a rewarding policy by executing another policy that gathers samples of experience. While the former policy (i.e. target policy) is rewarding but in-expressive (in most cases, deterministic), doing well in the latter task, in contrast, requires an expressive policy (i.e. behavior policy) that offers guided and effective exploration. Contrary to most methods that make a trade-off between optimality and expressiveness, disentangled frameworks explicitly decouple the two objectives, which each is dealt with by a distinct separate policy. Although being able to freely design and optimize the two policies with respect to their own objectives, naively disentangling them can lead to inefficient learning or stability issues. To mitigate this problem, our proposed method Analogous Disentangled Actor-Critic (ADAC) designs analogous pairs of actors and critics. Specifically, ADAC leverages a key property about Stein variational gradient descent (SVGD) to constraint the expressive energy-based behavior policy with respect to the target one for effective exploration. Additionally, an analogous critic pair is introduced to incorporate intrinsic rewards in a principled manner, with theoretical guarantees on the overall learning stability and effectiveness. We empirically evaluate environment-reward-only ADAC on 14 continuous-control tasks and report the state-of-the-art on 10 of them. We further demonstrate ADAC, when paired with intrinsic rewards, outperform alternatives in exploration-challenging tasks.
Incorporating Effective Global Information via Adaptive Gate Attention  for Text Classification

The dominant text classification studies focus on training classifiers using textual instances only or introducing external knowledge (e.g., hand-craft features and domain expert knowledge). In contrast, some corpus-level statistical features, like word frequency and distribution, are not well exploited. Our work shows that such simple statistical information can enhance classification performance both efficiently and significantly compared with several baseline models. In this paper, we propose a classifier with gate mechanism named Adaptive Gate Attention model with Global Information (AGA+GI), in which the adaptive gate mechanism incorporates global statistical features into latent semantic features and the attention layer captures dependency relationship within the sentence. To alleviate the overfitting issue, we propose a novel Leaky Dropout mechanism to improve generalization ability and performance stability. Our experiments show that the proposed method can achieve better accuracy than CNN-based and RNN-based approaches without global information on several benchmarks.
On the Convergence of the Monte Carlo Exploring Starts Algorithm for  Reinforcement Learning

A simple and natural algorithm for reinforcement learning is Monte Carlo Exploring States (MCES), where the Q-function is estimated by averaging the Monte Carlo returns, and the policy is improved by choosing actions that maximize the current estimate of the Q-function. Exploration is performed by "exploring starts", that is, each episode begins with a randomly chosen state and action and then follows the current policy. Establishing convergence for this algorithm has been an open problem for more than 20 years. We make headway with this problem by proving convergence for Optimal Policy Feed-Forward MDPs, which are MDPs whose states are not revisited within any episode for an optimal policy. Such MDPs include all deterministic environments (including Cliff Walking and other gridworld examples) and a large class of stochastic environments (including Blackjack). The convergence results presented here make progress for this long-standing open problem in reinforcement learning.
PREHEATING EFFECT OF GIOMERâ€™S SURFACE HARDNESS ON DIRECT RESTORATION

Background: Composite resin is a material that is widely used for caries restoration, enamel abrasion, and aesthetic needs because of the good fit with teeth. Restorative materials that release fluoride ions may prolong the therapeutic and protective effects of the enamel. Nowadays, composite resins have been developed a new hybrid restorative material, named giomer. Giomer containing pre-reacted glass-ionomer (PRG) which can release and recharge fluoride ions. Composite resins have a disadvantage, a presence of shrinkage during polymerization, which can cause gaps between the tooth surface and composite resin. Preheating is carried out before irradiation with conventional oven. Preheating makes the composite resin stronger, reduces viscosity makes it easier to adapt and apply, and improves mechanical properties. This study aims to determine the effect of preheating on surface hardness. Method: The research sample used is 27 pieces cylinder-shaped with 5 mm diameter and 2 mm depth. The number of treatments was divided into 3 treatment groups. The treatment groups were divided based on the preheating treatment temperature of 37°C, 60°C, and control group. The surface hardness test was carried out using Vicker's microhardness tester. Result: The statistical One Way ANOVA test showed a significant result between the groups (p&lt;0,05). LSD test (Least Significance Difference test) showed a significant result between the treatment temperature of 37°C, 60°C, and control group (p&lt;0,05). Conclusion: The result of the giomer surface hardness test with preheating showed a higher result than the control group. The preheating group with a temperature of 60°C had the highest result.
Perspectives on Socially Intelligent Conversational Agents

The propagation of digital assistants is consistently progressing. Manifested by an uptake of ever more human-like conversational abilities, respective technologies are moving increasingly away from their role as voice-operated task enablers and becoming rather companion-like artifacts whose interaction style is rooted in anthropomorphic behavior. One of the required characteristics in this shift from a utilitarian tool to an emotional character is the adoption of social intelligence. Although past research has recognized this need, more multi-disciplinary investigations should be devoted to the exploration of relevant traits and their potential embedding in future agent technology. Aiming to lay a foundation for further developments, we report on the results of a Delphi study highlighting the respective opinions of 21 multi-disciplinary domain experts. Results exhibit 14 distinctive characteristics of social intelligence, grouped into different levels of consensus, maturity, and abstraction, which may be considered a relevant basis, assisting the definition and consequent development of socially intelligent conversational agents.
Research on the Auxiliary Application of Computer Big Data Technology in Digital Children's Books

Children's literature is one of the important media for children to recognize and perceive things around them, and it plays an important role in children's growth and personality development. In response to the reading needs of children's literature books on mobile platforms, based on the Android system and the MVC framework, using application components such as Activity, Broad Receiver, Service, etc., by calling application functions such as Media Player, Media Scanner, Mark Adapter, My dialog, etc., Combined with the artistic characteristics of traditional children's literature books and the requirements of cross-border development of mobile platforms, multi-sensory design, multi-platform application and interactive experience, digital electronic literature books that combine traditional children's literature books and multimedia technology are developed and designed. Transforming the single reading method of traditional literary books into an immersive experience combining dynamic and static is of great significance for improving children's reading interest and promoting children's perceptual ability.
Design and Implementation of Knowledge Extraction in Geographic Knowledge Graphs

The infrastructure construction of the electrical power system will produce a large amount of geographic information data. Knowledge graphs is an intelligent database integrating artificial intelligence technology and traditional database, which is used for the structured management of large-scale knowledge. The combination of geographic know-ledge graph and electrical power field enables the power system to mine and analyze useful knowledge in large-scale geographic information. This paper systematically reviewed the research status of know-ledge graphs and geographic knowledge graphs, putting forward the construction process of geographic knowledge graphs, and focusing on the design and implementation of knowledge extraction in power transmission and transformation projects. Finally, based on the seed construction method, this paper uses optimal algorithm to train the geographic information dataset, where the ability of processing tremendous information can be enhanced significantly.
SLPL-Sentiment at SemEval-2022 Task 10: Making Use of Pre-Trained Model’s Attention Values in Structured Sentiment Analysis

Sentiment analysis is a useful problem which could serve a variety of fields from business intelligence to social studies and even health studies.Using SemEval 2022 Task 10 formulation of this problem and taking sequence labelling as our approach, we propose a model which learns the task by finetuning a pretrained transformer, introducing as few parameters ( 150k) as possible and making use of precomputed attention values in the transformer.Our model improves shared task baselines on all task datasets.
SSN_NLP_MLRG at SemEval-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending Language

In this paper, we describe our efforts at Se-mEval 2022 Shared Task 4 on Patronizing and Condescending Language (PCL) Detection.This is the first shared task to detect PCL which is to identify and categorize PCL language towards vulnerable communities.The shared task consists of two subtasks: Patronizing and Condescending language detection (Subtask A) which is the binary task classification and identifying the PCL categories that express the condescension (Subtask B) which is the multi-label text classification.For PCL language detection, We proposed the ensemble strategies of a system combination of BERT, Roberta, Distilbert, Roberta large, Albert achieved the official results for Subtask A with a macro f1 score of 0.5172 on the test set which is improved by baseline score.For PCL Category identification, We proposed a multi-label classification model to ensemble the various Bert-based models and the official results for Subtask B with a macro f1 score of 0.2117 on the test set which is improved by baseline score.
Team LRL_NC at SemEval-2022 Task 4: Binary and Multi-label Classification of PCL using Fine-tuned Transformer-based Models

Patronizing and condescending language (PCL) can find its way into many mediums of public discourse.Presence of PCL in text can produce negative effects in the society.The challenge presented by the task emerges from the subtleties of PCL and various data dependent constraints.Hence, developing techniques to detect PCL in text, before it is propagated is vital.The aim of this paper is twofold, a) to present systems that can be used to classify a text as containing PCL or not, and b) to present systems that assign the different categories of PCL present in text.The proposed systems are primarily rooted in transformer-based pretrained language models.Among the models submitted for Subtask 1, the best F1-Score of 0.5436 was achieved by a deep learning based ensemble model.This system secured the rank 29 in the official task ranking.For Subtask 2, the best macro-average F1-Score of 0.339 was achieved by an ensemble model combining transformer-based neural architecture with gradient boosting label-balanced classifiers.This system secured the rank 21 in the official task ranking.Among subsequently carried out experiments a variation in architecture of a system for Subtask 2 achieved a macro-average F1-Score of 0.3527.
I2C at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Deep Learning Techniques

Patronizing and Condescending Language is an ever-present problem in our day-today lives.There has been a rise in patronizing language on social media platforms manifesting itself in various forms.This paper presents two performing deep learning algorithms and results for the "Task 4: Patronizing and Condescending Language Detection." of SemEval 2022.The task incorporates an English dataset containing sentences from social media from around the world.The paper focuses on data augmentation to boost results on various deep learning methods as BERT and LSTM Neural Network.
Logical Transductions for the Typology of Ditransitive Prosody

Given the empirical landscape of possible prosodic parses, this paper examines the computations required to formalize the mapping from syntactic structure to prosodic structure.In particular, we use logical tree transductions to define the prosodic mapping of ditransitive verb phrases in SVO languages, building off of the typology described in Kalivoda (2018).Explicit formalization of syntax-prosody mapping revealed a number of unanswered questions relating to the fine details of theoretical assumptions behind prosodic mapping.
Incorporating Subjectivity into Gendered Ambiguous Pronoun (GAP) Resolution using Style Transfer

The GAP dataset is a Wikipedia-based evaluation dataset for gender bias detection in coreference resolution, containing mostly objective sentences. Since subjectivity is ubiquitous in our daily texts, it becomes necessary to evaluate models for both subjective and objective instances. In this work, we present a new evaluation dataset for gender bias in coreference resolution, GAP-Subjective, which increases the coverage of the original GAP dataset by including subjective sentences. We outline the methodology used to create this dataset. Firstly, we detect objective sentences and transfer them into their subjective variants using a sequence-to-sequence model. Secondly, we outline the thresholding techniques based on fluency and content preservation to maintain the quality of the sentences. Thirdly, we perform automated and human-based analysis of the style transfer and infer that the transferred sentences are of high quality. Finally, we benchmark both GAP and GAP-Subjective datasets using a BERT-based model and analyze its predictive performance and gender bias.
Logical Story Representations via FrameNet + Semantic Parsing

We propose a means of augmenting FrameNet parsers with a formal logic parser to obtain rich semantic representations of events.These schematic representations of the frame events, which we call Episodic Logic (EL) schemas, abstract constants to variables, preserving their types and relationships to other individuals in the same text.Due to the temporal semantics of the chosen logical formalism, all identified schemas in a text are also assigned temporally bound "episodes" and related to one another in time.The semantic role information from the FrameNet frames is also incorporated into the schema's type constraints.We describe an implementation of this method using a neural FrameNet parser, and discuss the approach's possible applications to question answering and open-domain event schema learning.
Sharing Data by Language Family: Data Augmentation for Romance Language Morpheme Segmentation

This paper presents a basic character level sequence-to-sequence approach to morpheme segmentation for the following Romance languages: French, Italian, and Spanish.We experiment with adding a small set of additional linguistic features, as well as with sharing training data between sister languages for morphological categories with low performance in single language base models.We find that while the additional linguistic features were generally not helpful in this instance, data augmentation between sister languages did help to raise the scores of some individual morphological categories, but did not consistently result in an overall improvement when considering the aggregate of the categories.
SSN_MLRG1 at SemEval-2022 Task 10: Structured Sentiment Analysis using 2-layer BiLSTM

Task 10 in SemEval 2022 is a composite task which entails analysis of opinion tuples, and recognition and demarcation of their nature.In this paper, we will elaborate on how such a methodology is implemented, how it is undertaken for a Structured Sentiment Analysis, and the results obtained thereof.To achieve this objective, we have adopted a bi-layered BiLSTM approach.In our research, a variation on the norm has been effected towards enhancement of accuracy, by basing the categorization meted out to an individual member as a by-product of its adjacent members, using specialized algorithms to ensure the veracity of the output, which has been modelled to be the holistically most accurate label for the entire sequence.Such a strategy is superior in terms of its parsing accuracy and requires less time.This manner of action has yielded an SF1 of 0.33 in the highest-performing configuration.
Interactive Query-Assisted Summarization via Deep Reinforcement Learning

Interactive summarization is a task that facilitates user-guided exploration of information within a document set.While one would like to employ state of the art neural models to improve the quality of interactive summarization, many such technologies cannot ingest the full document set or cannot operate at sufficient speed for interactivity.To that end, we propose two novel deep reinforcement learning models for the task that address, respectively, the subtask of summarizing salient information that adheres to user queries, and the subtask of listing suggested queries to assist users throughout their exploration. 1In particular, our models allow encoding the interactive session state and history to refrain from redundancy.Together, these models compose a state of the art solution that addresses all of the task requirements.We compare our solution to a recent interactive summarization system, and show through an experimental study involving real users that our models are able to improve informativeness while preserving positive user experience.
Pre-trained Language Models’ Interpretation of Evaluativity Implicature: Evidence from Gradable Adjectives Usage in Context

By saying Maria is tall, a human speaker typically implies that Maria is evaluatively tall from the speaker’s perspective. However, by using a different construction Maria is taller than Sophie, we cannot infer from Maria and Sophie’s relative heights that Maria is evaluatively tall because it is possible for Maria to be taller than Sophie in a context in which they both count as short. Can pre-trained language models (LMs) “understand” evaulativity (EVAL) inference? To what extent can they discern the EVAL salience of different constructions in a conversation? Will it help LMs’ implicitness performance if we give LMs a persona such as chill, social, and pragmatically skilled? Our study provides an approach to probing LMs’ interpretation of EVAL inference by incorporating insights from experimental pragmatics and sociolinguistics. We find that with the appropriate prompt, LMs can succeed in some pragmatic level language understanding tasks. Our study suggests that socio-pragmatics methodology can shed light on the challenging questions in NLP.
BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencoders

This paper describes our two deep learning systems that competed at SemEval-2022 Task 1 "CODWOE: Comparing Dictionaries and WOrd Embeddings".We participated in the subtask for the reverse dictionary which consists in generating vectors from glosses.We use sequential models that integrate several neural networks, starting from Embeddings networks until the use of Dense networks, Bidirectional Long Short-Term Memory (BiLSTM) networks and LSTM networks.All glosses have been preprocessed in order to consider the best representation form of the meanings for all words that appears.We achieved very competitive results in reverse dictionary with a second position in English and French languages when using contextualized embeddings, and the same position for English, French and Spanish languages when using char embeddings.Our source code can be found at GitHub 1 .
Users Hate Blondes: Detecting Sexism in User Comments on Online Romanian News

Romania ranks almost last in Europe when it comes to gender equality in political representation, with about 10% fewer women in politics than the E.U. average.We proceed from the assumption that this underrepresentation is also influenced by the sexism and verbal abuse female politicians face in the public sphere, especially in online media.We propose a novel dataset with sexist comments in Romanian language from online newspaper articles about Romanian female politicians and experiment with baseline models using classical machine learning models and fine-tuned pre-trained transformer models for the classification of sexist language in the online medium.
DANGNT-SGU at SemEval-2022 Task 11: Using Pre-trained Language Model for Complex Named Entity Recognition

This paper describes a system that we built to participate in the SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition, specifically the track Mono-lingual in English.To construct this system, we used Pre-trained Language Models (PLMs).Especially, the Pre-trained Model base on BERT is applied for the task of recognizing named entities by fine-tuning method.We performed the evaluation on two test datasets of the shared task: the Practice Phase and the Evaluation Phase of the competition.
Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition

Named Entity Recognition (NER), an essential subtask in NLP that identifies text belonging to predefined semantics such as a person, location, organization, drug, time, clinical procedure, biological protein, etc. NER plays a vital role in various fields such as information extraction, question answering, and machine translation.This paper describes our participating system run to the Named entity recognition and classification shared task SemEval-2022.The task is motivated towards detecting semantically ambiguous and complex entities in short and low-context settings.Our team focused on improving entity recognition by improving the word embeddings.We concatenated the word representations from State-of-the-art language models and passed them to find the best representation through a reinforcement trainer.Our results highlight the improvements achieved by various embedding concatenations.
Complex Word Identification in Vietnamese: Towards Vietnamese Text Simplification

Text Simplification has been an extensively researched problem in English, but has not been investigated in Vietnamese.We focus on the Vietnamese-specific Complex Word Identification task, often the first step in Lexical Simplification (Shardlow, 2013).We examine three different Vietnamese datasets constructed for other natural language processing tasks and show that, like in other languages, frequency is a strong signal in determining whether a word is complex, with a mean accuracy of 86.87%.Across the datasets, we find that the 10% most frequent words in many corpus can be labeled as simple, and the rest as complex, though this is more variable for smaller corpora.We also examine how human annotators perform at this task.Given the subjective nature, there is a fair amount of variability in which words are seen as difficult, though majority results are more consistent.