Reviewerly/exHarmony huggingface dataset:
GRACE: Gradient-guided Controllable Retrieval for Augmenting Attribute-based Text Generation

Attribute-based generation methods are of growing significance in controlling the generation of large pre-trained language models (PLMs).Existing studies control the generation by (1) finetuning the model with attributes or (2) guiding the inference processing toward control signals while freezing the PLM.However, finetuning approaches infuse domain bias into generation, making it hard to generate out-of-domain texts.Besides, many methods guide the inference in its word-by-word generation, pushing the word probability to the target attributes, resulting in less fluent sentences.We argue that distilling controlling information from natural texts can produce fluent sentences while maintaining high controllability.In this paper, we propose GRAdient-guided Controllable rEtrieval (GRACE), a retrievalaugmented generation framework to facilitate the generation of fluent sentences with high attribute relevance.GRACE memorizes the semantic and attribute information from unlabeled corpora and applies a controllable retrieval to obtain desired information.For the generation, we design techniques to eliminate the domain bias from the retrieval results and integrate it into the generation model.Additionally, we propose a gradient-guided generation scheme that iteratively steers generation toward higher attribute relevance.Experimental results and quantities of examples verify the effectiveness of our method.
Dialogue Planning via Brownian Bridge Stochastic Process for Goal-directed Proactive Dialogue

Goal-directed dialogue systems aim to proactively reach a pre-determined target through multi-turn conversations.The key to achieving this task lies in planning dialogue paths that smoothly and coherently direct conversations towards the target.However, this is a challenging and under-explored task.In this work, we propose a coherent dialogue planning approach that uses a stochastic process to model the temporal dynamics of dialogue paths.We define a latent space that captures the coherence of goal-directed behavior using a Brownian bridge process, which allows us to incorporate user feedback flexibly in dialogue planning.Based on the derived latent trajectories, we generate dialogue paths explicitly using pre-trained language models.We finally employ these paths as natural language prompts to guide dialogue generation.Our experiments show that our approach generates more coherent utterances and achieves the goal with a higher success rate 1 .* Equal contribution. 1 Our code and data are available at https://github.com/iwangjian/Color4Dial.Hello, how are you going today?
When and how to paraphrase for named entity recognition?

While paraphrasing is a promising approach for data augmentation in classification tasks, its effect on named entity recognition (NER) is not investigated systematically due to the difficulty of span-level label preservation.In this paper, we utilize simple strategies to annotate entity spans in generations and compare established and novel methods of paraphrasing in NLP such as back translation, specialized encoderdecoder models such as Pegasus, and GPT-3 variants for their effectiveness in improving downstream performance for NER across different levels of gold annotations and paraphrasing strength on 5 datasets.We thoroughly explore the influence of paraphrasers, dynamics between paraphrasing strength and gold dataset size on the NER performance with visualizations and statistical testing.We find that the choice of the paraphraser greatly impacts NER performance, with one of the larger GPT-3 variants exceedingly capable of generating high quality paraphrases, yielding statistically significant improvements in NER performance with increasing paraphrasing strength, while other paraphrasers show more mixed results.Additionally, inline auto annotations generated by larger GPT-3 are strictly better than heuristic based annotations.We also find diminishing benefits of paraphrasing as gold annotations increase for most datasets.Furthermore, while most paraphrasers promote entity memorization in NER, the proposed GPT-3 configuration performs most favorably among the compared paraphrasers when tested on unseen entities, with memorization reducing further with paraphrasing strength.Finally, we explore mention replacement using GPT-3, which provides additional benefits over base paraphrasing for specific datasets.
Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination

Large-scale pretrained language models have made significant advances in solving downstream language understanding tasks.However, they generally suffer from reporting bias, the phenomenon describing the lack of explicit commonsense knowledge in written text, e.g., "an orange is orange".To overcome this limitation, we develop a novel approach, Z-LaVI, to endow language models with visual imagination capabilities.Specifically, we leverage two complementary types of "imaginations": (i) recalling existing images through retrieval and (ii) synthesizing nonexistent images via text-toimage generation.Jointly exploiting the language inputs and the imagination, a pretrained vision-language model (e.g., CLIP) eventually composes a zero-shot solution to the original language tasks.Notably, fueling language models with imagination can effectively leverage visual knowledge to solve plain language tasks.In consequence, Z-LaVI consistently improves the zero-shot performance of existing language models across a diverse set of language tasks. 1 * Work was done during the internship at Tencent AI Lab.
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

African languages are spoken by over a billion people, but are underrepresented in NLP research and development.The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settings where current methods are effective.In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER).We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance.We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English.Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages.
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning

Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a taskoriented dialog system.Traditional softmaxbased confidence scores are susceptible to the overconfidence issue.In this paper, we propose a simple but strong energy-based score function to detect OOD where the energy scores of OOD samples are higher than IND samples.Further, given a small set of labeled OOD samples, we introduce an energy-based margin objective for supervised OOD detection to explicitly distinguish OOD samples from INDs.Comprehensive experiments and analysis prove our method helps disentangle confidence score distributions of IND and OOD data. 1
CGF: Constrained Generation Framework for Query Rewriting in Conversational AI

In conversational AI agents, Query Rewriting (QR) plays a crucial role in reducing user frictions and satisfying their daily demands.User frictions are caused by various reasons, such as errors in the conversational AI system, users' accent or their abridged language.In this work, we present a novel Constrained Generation Framework (CGF) for query rewriting at both global and personalized levels.It is based on the encoder-decoder framework, where the encoder takes the query and its previous dialogue turns as the input to form a contextenhanced representation, and the decoder uses constrained decoding to generate the rewrites based on the pre-defined global or personalized constrained decoding space.Extensive offline and online A/B experiments show that the proposed CGF significantly boosts the query rewriting performance.
A Span-based Multimodal Variational Autoencoder for Semi-supervised Multimodal Named Entity Recognition

Multimodal named entity recognition (MNER) on social media is a challenging task which aims to extract named entities in free text and incorporate images to classify them into userdefined types.The existing semi-supervised named entity recognition methods focus on the text modal and are utilized to reduce labeling costs in traditional NER.However, the previous methods are not efficient for semisupervised MNER.Because the MNER task is defined to combine the text information with image one and needs to consider the mismatch between the posted text and image.To fuse the text and image features for MNER effectively under semi-supervised setting, we propose a novel span-based multimodal variational autoencoder (SMVAE) model for semisupervised MNER.The proposed method exploits modal-specific VAEs to model text and image latent features, and utilizes product-ofexperts to acquire multimodal features.In our approach, the implicit relations between labels and multimodal features are modeled by multimodal VAE.Thus, the useful information of unlabeled data can be exploited in our method under semi-supervised setting.Experimental results on two benchmark datasets demonstrate that our approach not only outperforms baselines under supervised setting, but also improves MNER performance with less labeled data than existing semi-supervised methods.
TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering

Unsupervised commonsense question answering requires mining effective commonsense knowledge without the rely on the labeled task data.Previous methods typically retrieved from traditional knowledge bases or used pre-trained language models (PrLMs) to generate fixed types of knowledge, which have poor generalization ability.In this paper, we aim to address the above limitation by leveraging the implicit knowledge stored in PrLMs and propose a two-stage prompt-based unsupervised commonsense question answering framework (TSGP).Specifically, we first use knowledge generation prompts to generate the knowledge required for questions with unlimited types and possible candidate answers independent of specified choices.Then, we further utilize answer generation prompts to generate possible candidate answers independent of specified choices.Experimental results and analysis on three different commonsense reasoning tasks, CommonsenseQA, OpenBookQA, and SocialIQA, demonstrate that TSGP significantly improves the reasoning ability of language models in unsupervised settings 1 .
PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance

To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC.Compared with existing CQA datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical reasoning, and (iii) hybrid context of tables and text.A new task is defined accordingly to study Proactive Conversational Question Answering (PCQA), which combines clarification question generation and CQA.In addition, we propose a novel method, namely UniPCQA, to adapt a hybrid format of input and output content in PCQA into the Seq2Seq problem, including the reformulation of the numerical reasoning process as code generation.UniPCQA performs multi-task learning over all sub-tasks in PCQA and incorporates a simple ensemble strategy to alleviate the error propagation issue in the multi-task learning by cross-validating top-k sampled Seq2Seq outputs.We benchmark the PACIFIC dataset with extensive baselines and provide comprehensive evaluations on each sub-task of PCQA.
Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer?

Multilingual BERT (mBERT) has demonstrated considerable cross-lingual syntactic ability, whereby it enables effective zero-shot cross-lingual transfer of syntactic knowledge.The transfer is more successful between some languages, but it is not well understood what leads to this variation and whether it fairly reflects difference between languages.In this work, we investigate the distributions of grammatical relations induced from mBERT in the context of 24 typologically different languages.We demonstrate that the distance between the distributions of different languages is highly consistent with the syntactic difference in terms of linguistic formalisms.Such difference learnt via self-supervision plays a crucial role in the zero-shot transfer performance and can be predicted by variation in morphosyntactic properties between languages.These results suggest that mBERT properly encodes languages in a way consistent with linguistic diversity and provide insights into the mechanism of crosslingual transfer.
Investigating Math Word Problems using Pretrained Multilingual Language Models

In this paper, we revisit math word problems (MWPs) from the cross-lingual and multilingual perspective.We construct our MWP solvers over pretrained multilingual language models using the sequence-to-sequence model with copy mechanism.We compare how the MWP solvers perform in cross-lingual and multilingual scenarios.To facilitate the comparison of cross-lingual performance, we first adapt the large-scale English dataset MathQA as a counterpart of the Chinese dataset Math23K.Then we extend several English datasets to bilingual datasets through machine translation plus human annotation.Our experiments show that the MWP solvers may not be transferred to a different language even if the target expressions share the same numerical constants and operator set.However, it can be better generalized if problem types exist on both source language and target language.
PM2F2N: Patient Multi-view Multi-modal Feature Fusion Networks for Clinical Outcome Prediction

Clinical outcome prediction is critical to the condition prediction of patients and management of hospital capacities.There are two kinds of medical data, including time series signals recorded by various devices and clinical notes in electronic health records (EHR), which are used for two common prediction targets: mortality and length of stay.Traditional methods focused on utilizing time series data but ignored clinical notes.With the development of deep learning, natural language processing (NLP) and multi-modal learning methods are exploited to jointly model the time series and clinical notes with different modals.However, the existing methods failed to fuse the multi-modal features of patients from different views.Therefore, we propose the patient multi-view multi-modal feature fusion networks for clinical outcome prediction.Firstly, from patient inner view, we propose to utilize the co-attention module to enhance the fine-grained feature interaction between time series and clinical notes from each patient.Secondly, the patient outer view is the correlation between patients, which can be reflected by the structural knowledge in clinical notes.We exploit the structural information extracted from clinical notes to construct the patient correlation graph, and fuse patients' multi-modal features by graph neural networks (GNN).The experimental results on MIMIC-III benchmark demonstrate the superiority of our method.
Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence

AI researchers have posited Dungeons and Dragons (D&D) as a challenge problem to test systems on various language-related capabilities.In this paper, we frame D&D specifically as a dialogue system challenge, where the tasks are to both generate the next conversational turn in the game and predict the state of the game given the dialogue history.We create a gameplay dataset consisting of nearly 900 games, with a total of 7,000 players, 800,000 dialogue turns, 500,000 dice rolls, and 58 million words.We automatically annotate the data with partial state information about the game play.We train a large language model (LM) to generate the next game turn, conditioning it on different information.The LM can respond as a particular character or as the player who runs the game-i.e., the Dungeon Master (DM).It is trained to produce dialogue that is either in-character (roleplaying in the fictional world) or out-of-character (discussing rules or strategy).We perform a human evaluation to determine what factors make the generated output plausible and interesting.We further perform an automatic evaluation to determine how well the model can predict the game state given the history and examine how well tracking the game state improves its ability to produce plausible conversational output.
Subspace distillation for continual learning

An ultimate objective in continual learning is to preserve knowledge learned in preceding tasks while learning new tasks. To mitigate forgetting prior knowledge, we propose a novel knowledge distillation technique that takes into the account the manifold structure of the latent/output space of a neural network in learning novel tasks. To achieve this, we propose to approximate the data manifold up-to its first order, hence benefiting from linear subspaces to model the structure and maintain the knowledge of a neural network while learning novel concepts. We demonstrate that the modeling with subspaces provides several intriguing properties, including robustness to noise and therefore effective for mitigating Catastrophic Forgetting in continual learning. We also discuss and show how our proposed method can be adopted to address both classification and segmentation problems. Empirically, we observe that our proposed method outperforms various continual learning methods on several challenging datasets including Pascal VOC, and Tiny-Imagenet. Furthermore, we show how the proposed method can be seamlessly combined with existing learning approaches to improve their performances. The codes of this article will be available at https://github.com/csiro-robotics/SDCL.
Computing geodesic paths encoding a curvature prior for curvilinear structure tracking

In this paper, we introduce an efficient method for computing curves minimizing a variant of the Euler-Mumford elastica energy, with fixed endpoints and tangents at these endpoints, where the bending energy is enhanced with a user-defined and data-driven scalar-valued term referred to as the curvature prior. In order to guarantee that the globally optimal curve is extracted, the proposed method involves the numerical computation of the viscosity solution to a specific static Hamilton-Jacobi-Bellman (HJB) partial differential equation (PDE). For that purpose, we derive the explicit Hamiltonian associated with this variant model equipped with a curvature prior, discretize the resulting HJB PDE using an adaptive finite difference scheme, and solve it in a single pass using a generalized fast-marching method. In addition, we also present a practical method for estimating the curvature prior values from image data, designed for the task of accurately tracking curvilinear structure centerlines. Numerical experiments on synthetic and real-image data illustrate the advantages of the considered variant of the elastica model with a prior curvature enhancement in complex scenarios where challenging geometric structures appear.
Exploring the availability, protocols and advertising of Tor v3 domains

This study analyzes the availability of Tor onion services over time, as they have a limited lifetime and can be activated and deactivated intermittently. Various onion services use different protocols, which are analyzed, including 15 in total. The presence of tor links in online resources for advertising is also characterized. Having collected 54,602 onion addresses for 6 months, the experimental analysis shows that 23.65% were dead and 76.35% were alive, of which 32.74% were deactivated and activated at some point in a window of 31 days. In terms of protocols, HTTP was observed to be predominant, followed by SSH and SMTP. In advertising, 49.72% of the links are found on the surface, 5.79% on the dark, and 44.49% on both, finding a large number of link aggregator repositories.
Grouped ABC for Feature Selection and Mean-Variance Optimization for Rule Mining: A Hybrid Framework

Data mining has become a popular process in recent times.However, with the increase in data, traditional data mining methods are not sufficient to solve many problems.Therefore, advanced techniques are needed to provide better results without consuming more time during execution.Soft computing algorithms are used for mathematical optimization to achieve better results in less time.The primary purpose of this work is to propose a framework for rule mining that shall generalize the currently applied methods in rule mining.In this respect, this paper represents the R-miner using a soft computing algorithm.The Grouped -Artificial Bee Colony Optimization (G-ABC) was used to select the relevant attribute set and further verify the features.Mean-Variance optimization is used to find whether the selected rule is valid for further classification.Furthermore, a neural-based deep learning method is applied to validate the outcome.The investigation outcome indicates that the proposed algorithm provides more optimized results in terms of the number of rules generated, the time required for calculation, and obtaining supplementary information for rule mining.
Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet

As one of the malignant tumors with high mortality, the initial symptoms of liver cancer are not obvious. In addition, the liver is the largest internal organ of the human body, and its structure and distribution are relatively complex. Therefore, in order to help doctors judge liver cancer more accurately, this paper proposes a variant model based on Unet network. Before segmentation, the image is preprocessed, and Pulse Coupled Neural Network (PCNN) algorithm is used to filter the image adaptively to make the image clearer. For the segmentation model, the SE module is used as the input of the residual network, and then its output is connected to the Unet model through bilinear interpolation to perform the down-sampling and up-sampling operations. The dataset is a combination of Hainan Provincial People's Hospital and some public datasets Lits. The results show that this method has better segmentation performance and accuracy than the original Unet method, and the dice coefficient, mIou and other evaluation indicators have increased by at least 2.1%, which is a method that can be applied to cancer segmentation.
Financial development and innovation: Do institutions and human capital matter?

<h2>Abstract</h2> This paper explores the dynamic link between financial development and innovation, while accounting for human capital and institutional quality for 29 OECD countries spanning 40 years (1980–2019). The findings based on Generalized Method of Moments (GMM) and the Fully Modified Ordinary Least Squares (FMOLS) estimators show that trade, institutional quality, human capital and financial development promote innovation activities, while foreign direct investment has the opposite effect. Further, the findings show a non-linear relationship between finance and innovation.