Reviewerly/exHarmony huggingface dataset:
You Are My Type! Type Embeddings for Pre-trained Language Models

One reason for the positive impact of Pre-trained Language Models (PLMs) in NLP tasks is their ability to encode semantic types, such as ‘European City’ or ‘Woman’. While previous work has analyzed such information in the context of interpretability, it is not clear how to use types to steer the PLM output. For example, in a cloze statement, it is desirable to steer the model to generate a token that satisfies a user-specified type, e.g., predict a date rather than a location. In this work, we introduce Type Embeddings (TEs), an input embedding that promotes desired types in a PLM. Our proposal is to define a type by a small set of word examples. We empirically study the ability of TEs both in representing types and in steering masking predictions without changes to the prompt text in BERT. Finally, using the LAMA datasets, we show how TEs highly improve the precision in extracting facts from PLMs.
Rule-Based Clause-Level Morphology for Multiple Languages

This paper describes an approach for the morphosyntactic analysis of clauses, including the analysis of composite verb forms and both overt and covert pronouns.The approach uses grammatical rules for verb inflection and clauseinternal word agreement to compute a clause's morphosyntactic features from the morphological features of the individual words.The approach is tested for eight typologically diverse languages in the 1st Shared Task on Multilingual Clause-Level Morphology, where it achieves F1 scores between 79% and 99% (94% in average).you are not wanting it from yourselves .
Assessing the Limits of Straightforward Models for Nested Named Entity Recognition in Spanish Clinical Narratives

Nested Named Entity Recognition (NER) is an information extraction task that aims to identify entities that may be nested within other entity mentions.Despite the availability of several corpora with nested entities in the Spanish clinical domain, most previous work has overlooked them due to the lack of models and a clear annotation scheme for dealing with the task.To fill this gap, this paper provides an empirical study of straightforward methods for tackling the nested NER task on two Spanish clinical datasets, Clinical Trials, and the Chilean Waiting List.We assess the advantages and limitations of two sequence labeling approaches; one based on Multiple LSTM-CRF architectures and another on Joint labeling models.To better understand the differences between these models, we compute taskspecific metrics that adequately measure the ability of models to detect nested entities and perform a fine-grained comparison across models.Our experimental results show that employing domain-specific language models trained from scratch significantly improves the performance obtained with strong domain-specific and general-domain baselines, achieving stateof-the-art results in both datasets.Specifically, we obtained F 1 scores of 89.21 and 83.16 in Clinical Trials and the Chilean Waiting List, respectively.Interestingly enough, we observe that the task-specific metrics and analysis properly reflect the limitations of the models when recognizing nested entities.Finally, we perform a case study on an aggregated NER dataset created from several clinical corpora in Spanish.We highlight how entity length and the simultaneous recognition of inner and outer entities are the most critical variables for the nested NER task.
Spectral Probing

Linguistic information is encoded at varying timescales (subwords, phrases, etc.) and communicative levels, such as syntax and semantics.Contextualized embeddings have analogously been found to capture these phenomena at distinctive layers and frequencies.Leveraging these findings, we develop a fully learnable frequency filter to identify spectral profiles for any given task.It enables vastly more granular analyses than prior handcrafted filters, and improves on efficiency.After demonstrating the informativeness of spectral probing over manual filters in a monolingual setting, we investigate its multilingual characteristics across seven diverse NLP tasks in six languages.Our analyses identify distinctive spectral profiles which quantify cross-task similarity in a linguistically intuitive manner, while remaining consistent across languages-highlighting their potential as robust, lightweight task descriptors.
Plug-and-Play Recipe Generation with Content Planning

Recent pre-trained language models have shown promising capabilities in generating fluent and realistic natural language text.However, generating multi-sentence text with global content planning has been a long-existing research question.Current approaches for controlled text generation can hardly address this issue, as they usually condition on single known control attributes.In this study, we propose a low-cost yet effective framework which explicitly models the global content plan of the generated text.Specifically, it optimizes the joint distribution of the natural language sequence and the global content plan in a plugand-play manner.We conduct extensive experiments on the well-established Recipe1M+ benchmark.Both automatic and human evaluations verify that our model achieves the stateof-the-art performance on the task of recipe generation. 1
Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?

The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) code prediction results of full-fledged deep learning-based methods.However, most previous SOTA works based on deep learning are still in early stages in terms of providing textual references and explanations of the predicted codes, despite the fact that this level of explainability of the prediction outcomes is critical to gaining trust from professional medical coders.This raises the important question of how well current explainability methods apply to advanced neural network models such as transformers to predict correct codes and present references in clinical notes that support code prediction.First, we present an explainable Read, Attend, and Code (xRAC) framework and assess two approaches, attention score-based xRAC-ATTN and modelagnostic knowledge-distillation-based xRAC-KD, through simplified but thorough humangrounded evaluations with SOTA transformerbased model, RAC.We find that the supporting evidence text highlighted by xRAC-ATTN is of higher quality than xRAC-KD whereas xRAC-KD has potential advantages in production deployment scenarios.More importantly, we show for the first time that, given the current state of explainability methodologies, using the SOTA medical codes prediction system still requires the expertise and competencies of professional coders, even though its prediction accuracy is superior to that of human coders.This, we believe, is a very meaningful step toward developing explainable and accurate machine learning systems for fully autonomous medical code prediction from clinical notes.
Boosting Document-Level Relation Extraction by Mining and Injecting Logical Rules

Document-level relation extraction (DocRE)aims at extracting relations of all entity pairs in a document.A key challenge to DocRE lies in the complex interdependency between the relations of entity pairs.Unlike most prior efforts focusing on implicitly powerful representations, the recently proposed LogiRE (Ru et al., 2021) explicitly captures the interdependency by learning logical rules.However, Lo-giRE requires extra parameterized modules to reason merely after training backbones, and this disjointed optimization of backbones and extra modules may lead to sub-optimal results.In this paper, we propose MILR, a logic enhanced framework that boosts DocRE by Mining and Injecting Logical Rules.MILR first mines logical rules from annotations based on frequencies.Then in training, consistency regularization is leveraged as an auxiliary loss to penalize instances that violate mined rules.Finally, MILR infers from a global perspective based on integer programming.Compared with LogiRE, MILR does not introduce extra parameters and injects logical rules during both training and inference.Extensive experiments on two benchmarks demonstrate that MILR not only improves the relation extraction performance (1.1%-3.8%F1) but also makes predictions more logically consistent (over 4.5% Logic).More importantly, MILR also consistently outperforms LogiRE on both counts.Code is available at https:// github.com/XingYing-stack/MILR.
Average Is Not Enough: Caveats of Multilingual Evaluation

This position paper discusses the problem of multilingual evaluation.Using simple statistics, such as average language performance, might inject linguistic biases in favor of dominant language families into evaluation methodology.We argue that a qualitative analysis informed by comparative linguistics is needed for multilingual results to detect this kind of bias.We show in our case study that results in published works can indeed be linguistically biased and we demonstrate that visualization based on URIEL typological database can detect it.
On the Effectiveness of Automated Metrics for Text Generation Systems

A major challenge in the field of Text Generation is evaluation, because we lack a sound theory that can be leveraged to extract guidelines for evaluation campaigns.In this work, we propose a first step towards such a theory that incorporates different sources of uncertainty, such as imperfect automated metrics and insufficiently sized test sets.The theory has practical applications, such as determining the number of samples needed to reliably distinguish the performance of a set of Text Generation systems in a given setting.We showcase the application of the theory on the WMT 21 and Spot-The-Bot evaluation data and outline how it can be leveraged to improve the evaluation protocol regarding the reliability, robustness, and significance of the evaluation outcome.
From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models

Investigating better ways to reuse the released pre-trained language models (PLMs) can significantly reduce the computational cost and the potential environmental sideeffects.This paper explores a novel PLM reuse paradigm, Knowledge Integration (KI).Without human annotations available, KI aims to merge the knowledge from different teacher-PLMs, each of which specializes in a different classification problem, into a versatile student model.To achieve this, we first derive the correlation between virtual golden supervision and teacher predictions.We then design a Model Uncertainty-aware Knowledge Integration (MUKI) framework to recover the golden supervision for the student.Specifically, MUKI adopts Monte-Carlo Dropout to estimate model uncertainty for the supervision integration.An instance-wise re-weighting mechanism based on the margin of uncertainty scores is further incorporated, to deal with the potential conflicting supervision from teachers.Experimental results demonstrate that MUKI achieves substantial improvements over baselines on benchmark datasets.Further analysis shows that MUKI can generalize well for merging teacher models with heterogeneous architectures, and even teachers major in cross-lingual datasets. 11 Our code is available at https://github.com/lancopku/MUKI.Part of the work was done while Yankai Lin and Peng Li were working at Tencent.
PAUQ: Text-to-SQL in Russian

Semantic parsing is an important task that allows to democratize human-computer interaction.One of the most popular text-to-SQL datasets with complex and diverse natural language (NL) questions and SQL queries is Spider.We construct and complement a Spider dataset for the Russian language, thus creating the first publicly available text-to-SQL dataset in Russian.While examining dataset components-NL questions, SQL queries, and database content-we identify limitations of the existing database structure, fill out missing values for tables and add new requests for underrepresented categories.We select thirty functional test sets with different features for evaluating the capabilities of neural models.To conduct the experiment, we adapt baseline models RAT-SQL and BRIDGE and provide in-depth query component analysis.Both models demonstrate strong single language results and improved accuracy with multilingual training on the target language.In this work, we also study tradeoffs between automatically translated and manually created NL queries.At present, Russian text-to-SQL is lacking in datasets as well as trained models, and we view this work as an important step toward filling this gap.
GiCCS: A German in-Context Conversational Similarity Benchmark

The Semantic textual similarity (STS) task is commonly used to evaluate the semantic representations that language models (LMs) learn from texts, under the assumption that goodquality representations will yield accurate similarity estimates.When it comes to estimating the similarity of two utterances in a dialogue, however, the conversational context plays a particularly important role.We argue for the need of benchmarks specifically created using conversational data in order to evaluate conversational LMs in the STS task.We introduce GiCCS, a first conversational STS evaluation benchmark for German.We collected the similarity annotations for GiCCS using best-worst scaling and presenting the target items in context, in order to obtain highly-reliable contextdependent similarity scores.We present benchmarking experiments for evaluating LMs on capturing the similarity of utterances.Results suggest that pretraining LMs on conversational data and providing conversational context can be useful for capturing similarity of utterances in dialogues.GiCCS is publicly available to encourage benchmarking of conversational LMs.
Weakly Supervised Headline Dependency Parsing

English news headlines form a register with unique syntactic properties that have been documented in linguistics literature since the 1930s.However, headlines have received surprisingly little attention from the NLP syntactic parsing community.We aim to bridge this gap by providing the first news headline corpus of Universal Dependencies annotated syntactic dependency trees, which enables us to evaluate existing state-of-the-art dependency parsers on news headlines.To improve English news headline parsing accuracies, we develop a projection method to bootstrap silver training data from unlabeled news headlinearticle lead sentence pairs.Models trained on silver headline parses demonstrate significant improvements in performance over models trained solely on gold-annotated long-form texts.Ultimately, we find that, although projected silver training data improves parser performance across different news outlets, the improvement is moderated by constructions idiosyncratic to outlet.
Efficient (Soft) Q-Learning for Text Generation with Limited Good Data

Maximum likelihood estimation (MLE) is the predominant algorithm for training text generation models.This paradigm relies on direct supervision examples, which is not applicable to many emerging applications, such as generating adversarial attacks or generating prompts to control language models.Reinforcement learning (RL) on the other hand offers a more flexible solution by allowing users to plug in arbitrary task metrics as reward.Yet previous RL algorithms for text generation, such as policy gradient (on-policy RL) and Q-learning (off-policy RL), are often notoriously inefficient or unstable to train due to the large sequence space and the sparse reward received only at the end of sequences.In this paper, we introduce a new RL formulation for text generation from the soft Q-learning (SQL) perspective.It enables us to draw from the latest RL advances, such as path consistency learning, to combine the best of on-/off-policy updates, and learn effectively from sparse reward.We apply the approach to a wide range of novel text generation tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation.Experiments show our approach consistently outperforms both task-specialized algorithms and the previous RL methods. 1
Quantum Radar: Challenges and Outlook: An Overview of the State of the Art

Quantum radar is a fledgling new technology that promises gains in detection performance. However, there are challenges standing in the way of their implementation. In this article, three classes of quantum radars are discussed: quantum interferometric radar, quantum illumination radar, and quantum two-mode squeezing (QTMS) radar. Of these, the last-named is the closest to being implemented in real life, though it is by no means the last word on the subject. In conclusion, we find that quantum radars are at least worth studying further.
Console Free Robot Chassis Using Hand Gesture Recognition

The world is now emerging towards automation and machine-based works which brings in the need for robots. One such kind of a robot which is of a huge need in the current era is the robotic chassis which has an enormous number of usages in various domains and fields.With advancements in technology, every sector is getting benefited just like the rescue operations team becoming more compact and smaller day by day. Miniature version of robots called the robotic chassis which is used for the analysis and studying of minute places and locations are coming into action slowly which can perform live tracking while recording HD video as well. A robotic chassis is an autonomous or semi-autonomous robot that can be controlled using a controller or a console to perform various operations such as pick and place, rescue bots, analysing and studying etc, However, the most important factor has not changed much for the controller. It is still heavy and unintuitive for beginners. Using hand gesture recognition via the open cv domain, the Robotic chassis can be controlled using identical operations performed through the gestures given as the input. Using real time working model, the hand gesture controlled robotic chassis is operated wireless for the first time using a Zigbee module. Hand gesture recognition provides the desired result for the desired posture that user previously trained using Deep learning technology. The output of hand gesture recognition is given as input to the robotic chassis microcontroller to calibrate the python code to the Arduino IDE. Basic robotic operations such as Forward, Backward, Left, Right and Stop operations can be performed based on input and simultaneously the live streaming video can be telecasted.
A Jeap-BiLSTM Neural Network for Action Recognition

Human action recognition in videos is an important task in computer vision with applications in fields such as surveillance, human–computer interaction, and sports analysis. However, it is a challenging task due to the complex background changes and redundancy of long-term video information. In this paper, we propose a novel bi-directional long short-term memory method with attention pooling based on joint motion and difference entropy (JEAP-BiLSTM) to address these challenges. To obtain discriminative features, we introduce a joint entropy map that measures both the entropy of motion and the entropy of change. The Bi-LSTM method is then applied to capture visual and temporal associations in both forward and backward directions, enabling efficient capture of long-term temporal correlation. Furthermore, attention pooling is used to highlight the region of interest and to mitigate the effects of background changes in video information. Experiments on the UCF101 and HMDB51 datasets demonstrate that the proposed JEAP-BiLSTM method achieves recognition rates of 96.4% and 75.2%, respectively, outperforming existing methods. Our proposed method makes significant contributions to the field of human action recognition by effectively capturing both spatial and temporal patterns in videos, addressing background changes, and achieving state-of-the-art performance.
Chapter 7. Neural-Symbolic Interaction and Co-Evolving

Symbolic knowledge is vital in various human cognitive functions, including reasoning, skill acquisition, and communication. Its integration is also essential for creating AI with human-like capabilities, such as robustness, creativity, and interpretability. Nevertheless, current machine learning approaches still dominantly emphasize learning from large data sets, struggling to effectively and scalably incorporate symbolic knowledge. This leads to fundamental limitations, such as brittle results when faced with complex or novel concepts, and difficulty in understanding or explaining the decision processes of models. Past attempts at integrating symbolic information with neural networks frequently rely on manually created knowledge bases that are defined in specific configurations, thereby impeding their generalizability to new applications and domains. This chapter seeks to introduce interaction and co-evolving mechanisms between neural models and symbolic knowledge bases. It starts from constructing a panoramic learning framework for learning with all experiences (data, rules, knowledge graphs, etc.). Subsequently, it delves into a novel inversion problem of extracting symbolic knowledge from black-box neural models. Finally, based on the components mentioned above, the chapter will explore a blueprint of a lifelong neural-symbolic system that accommodates human intervention.
Chapter 26. Combining Sub-Symbolic and Symbolic Methods for Explainability

Many important real-world data sets come in the form of graphs or networks, including social networks, knowledge graphs, protein-interaction networks, the World Wide Web and many more. Graph neural networks (GNNs) are connectionist models that capture the dependence structure induced by links via message passing between the nodes of graphs. Similarly to other connectionist models, GNNs lack transparency in their decision-making. Since the unprecedented levels of performance of such AI methods lead to increasing use in the daily life of humans, there is an emerging need to understand the decision-making process of such systems. While symbolic methods such as inductive logic learning come with explainability, they perform best when dealing with relatively small and precise data. Sub-symbolic methods such as graph neural networks are able to handle large datasets, have a higher tolerance to noise in real world data, generally have high computing performance and are easier to scale up. We aim to develop a hybrid method by combining GNNs, sub-symbolic explainer methods and inductive logic learning. This enables human-centric and causal explanations through extracting symbolic explanations from identified decision drivers and enriching them with available background knowledge. With this method, high-accuracy sub-symbolic predictions come with symbolic-level explanations, and the preliminary evaluation results reported show an effective solution for the performance vs. explainability trade-off. The evaluation is done on a chemical use case and an industrial cybersecurity use case.
The Clustering Effect Analysis of College Marxist Theory Network Propagation Based on Ant Colony Intelligent Algorithm

Abstract Marxist ideology is the mainstream ideology of China, and its effective transmission is to consolidate the ideological foundation and soul of the socialist system with Chinese characteristics. The effective transmission of Marxist thought in the new media environment will open up a new road for media development. In the development process of new media, it can work with mainstream ideology as the core. Based on the ant colony intelligence method, this paper discusses the effect of college Marxism theory on the Internet. The model uses an optimization method based on local feasibility and descending order to modify the pheromone of this method. The results show that the improved ant algorithm can efficiently distribute the load in Marx’s theoretical network.