pwc-archive/papers-with-abstracts huggingface dataset:
HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking
Authors: Yanzhao Zhang, Dingkun Long, Guangwei Xu, Pengjun Xie
Date: 2022-05-21 00:00:00
Tasks: Passage Ranking, Passage Re-Ranking, Reranking, Retrieval, Text Retrieval

Deep pre-trained language models (e,g. BERT) are effective at large-scale text retrieval task. Existing text retrieval systems with state-of-the-art performance usually adopt a retrieve-then-reranking architecture due to the high computational cost of pre-trained language models and the large corpus size. Under such a multi-stage architecture, previous studies mainly focused on optimizing single stage of the framework thus improving the overall retrieval performance. However, how to directly couple multi-stage features for optimization has not been well studied. In this paper, we design Hybrid List Aware Transformer Reranking (HLATR) as a subsequent reranking module to incorporate both retrieval and reranking stage features. HLATR is lightweight and can be easily parallelized with existing text retrieval systems so that the reranking process can be performed in a single yet efficient processing. Empirical experiments on two large-scale text retrieval datasets show that HLATR can efficiently improve the ranking performance of existing multi-stage text retrieval methods.
CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Models
Authors: Bishal Santra, Ravi Ghadia, Manish Gupta, Pawan Goyal
Date: 2022-05-21 00:00:00
Tasks: Reinforcement Learning (RL), Text Generation, valid

In the field of Natural Language Processing, there are many tasks that can be tackled effectively using the cross-entropy (CE) loss function. However, the task of dialog generation poses unique challenges for CE loss. This is because CE loss assumes that, for any given input, the only possible output is the one available as the ground truth in the training dataset. But, in dialog generation, there can be multiple valid responses (for a given context) that not only have different surface forms but can also be semantically different. Furthermore, CE loss computation for the dialog generation task does not take the input context into consideration and, hence, it grades the response irrespective of the context. To grade the generated response for qualities like relevance, engagingness, etc., the loss function should depend on both the context and the generated response. To address these limitations, this paper proposes CORAL, a novel loss function based on a reinforcement learning (RL) view of the dialog generation task with a reward function that estimates human preference for generated responses while considering both the context and the response. Furthermore, to overcome challenges such as high sample complexity of RL training and a large action space, we propose a mix-policy training algorithm. Notably, using CORAL we can train dialog generation models without assuming the ground-truth as the only correct response. Extensive comparisons on benchmark datasets demonstrate that CORAL based models outperform strong state-of-the-art baseline models of different sizes.
Pre-training Data Quality and Quantity for a Low-Resource Language: New Corpus and BERT Models for Maltese
Authors: Kurt Micallef, Albert Gatt, Marc Tanti, Lonneke van der Plas, Claudia Borg
Date: 2022-05-21 00:00:00
Proceeding: DeepLo 2022 7
Tasks: Cross-Lingual Transfer, Dependency Parsing, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Sentiment Analysis

Multilingual language models such as mBERT have seen impressive cross-lingual transfer to a variety of languages, but many languages remain excluded from these models. In this paper, we analyse the effect of pre-training with monolingual data for a low-resource language that is not included in mBERT -- Maltese -- with a range of pre-training set ups. We conduct evaluations with the newly pre-trained models on three morphosyntactic tasks -- dependency parsing, part-of-speech tagging, and named-entity recognition -- and one semantic classification task -- sentiment analysis. We also present a newly created corpus for Maltese, and determine the effect that the pre-training data size and domain have on the downstream performance. Our results show that using a mixture of pre-training domains is often superior to using Wikipedia text only. We also find that a fraction of this corpus is enough to make significant leaps in performance over Wikipedia-trained models. We pre-train and compare two models on the new corpus: a monolingual BERT model trained from scratch (BERTu), and a further pre-trained multilingual BERT (mBERTu). The models achieve state-of-the-art performance on these tasks, despite the new corpus being considerably smaller than typically used corpora for high-resourced languages. On average, BERTu outperforms or performs competitively with mBERTu, and the largest gains are observed for higher-level tasks.
Named Entity Linking with Entity Representation by Multiple Embeddings
Authors: Oleg Vasilyev, Alex Dauenhauer, Vedant Dharnidharka, John Bohannon
Date: 2022-05-21 00:00:00
Tasks: Entity Linking, Language Modeling, Language Modelling

We propose a simple and practical method for named entity linking (NEL), based on entity representation by multiple embeddings. To explore this method, and to review its dependency on parameters, we measure its performance on Namesakes, a highly challenging dataset of ambiguously named entities. Our observations suggest that the minimal number of mentions required to create a knowledge base (KB) entity is very important for NEL performance. The number of embeddings is less important and can be kept small, within as few as 10 or less. We show that our representations of KB entities can be adjusted using only KB data, and the adjustment can improve NEL performance. We also compare NEL performance of embeddings obtained from tuning language model on diverse news texts as opposed to tuning on more uniform texts from public datasets XSum, CNN / Daily Mail. We found that tuning on diverse news provides better embeddings.
Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection
Authors: Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti
Date: 2022-05-20 00:00:00
Tasks: Answer Selection, Question Answering, Sentence

An important task for designing QA systems is answer sentence selection (AS2): selecting the sentence containing (or constituting) the answer to a question from a set of retrieved relevant documents. In this paper, we propose three novel sentence-level transformer pre-training objectives that incorporate paragraph-level semantics within and across documents, to improve the performance of transformers for AS2, and mitigate the requirement of large labeled datasets. Specifically, the model is tasked to predict whether: (i) two sentences are extracted from the same paragraph, (ii) a given sentence is extracted from a given paragraph, and (iii) two paragraphs are extracted from the same document. Our experiments on three public and one industrial AS2 datasets demonstrate the empirical superiority of our pre-trained transformers over baseline models such as RoBERTa and ELECTRA for AS2.
Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms
Authors: Patrick Lee, Martha Gavidia, Anna Feldman, Jing Peng
Date: 2022-05-20 00:00:00
Proceeding: NAACL (unimplicit) 2022 7
Tasks: Sentence

This paper presents a linguistically driven proof of concept for finding potentially euphemistic terms, or PETs. Acknowledging that PETs tend to be commonly used expressions for a certain range of sensitive topics, we make use of distributional similarities to select and filter phrase candidates from a sentence and rank them using a set of simple sentiment-based metrics. We present the results of our approach tested on a corpus of sentences containing euphemisms, demonstrating its efficacy for detecting single and multi-word PETs from a broad range of topics. We also discuss future potential for sentiment-based methods on this task.
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts
Authors: Felix Drinkall, Stefan Zohren, Janet B. Pierrehumbert
Date: 2022-05-20 00:00:00
Proceeding: NAACL 2022 7
Tasks: Sentence, Time Series, Time Series Analysis, Time Series Forecasting

We present a novel approach incorporating transformer-based language models into infectious disease modelling. Text-derived features are quantified by tracking high-density clusters of sentence-level representations of Reddit posts within specific US states' COVID-19 subreddits. We benchmark these clustered embedding features against features extracted from other high-quality datasets. In a threshold-classification task, we show that they outperform all other feature types at predicting upward trend signals, a significant result for infectious disease modelling in areas where epidemiological data is unreliable. Subsequently, in a time-series forecasting task we fully utilise the predictive power of the caseload and compare the relative strengths of using different supplementary datasets as covariate feature sets in a transformer-based time-series model.
Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog
Authors: Chia-Chien Hung, Anne Lauscher, Ivan Vulić, Simone Paolo Ponzetto, Goran Glavaš
Date: 2022-05-20 00:00:00
Proceeding: NAACL 2022 7
Tasks: Cross-Lingual Transfer, dialog state tracking, Retrieval

Research on (multi-domain) task-oriented dialog (TOD) has predominantly focused on the English language, primarily due to the shortage of robust TOD datasets in other languages, preventing the systematic investigation of cross-lingual transfer for this crucial NLP application area. In this work, we introduce Multi2WOZ, a new multilingual multi-domain TOD dataset, derived from the well-established English dataset MultiWOZ, that spans four typologically diverse languages: Chinese, German, Arabic, and Russian. In contrast to concurrent efforts, Multi2WOZ contains gold-standard dialogs in target languages that are directly comparable with development and test portions of the English dataset, enabling reliable and comparative estimates of cross-lingual transfer performance for TOD. We then introduce a new framework for multilingual conversational specialization of pretrained language models (PrLMs) that aims to facilitate cross-lingual transfer for arbitrary downstream TOD tasks. Using such conversational PrLMs specialized for concrete target languages, we systematically benchmark a number of zero-shot and few-shot cross-lingual transfer approaches on two standard TOD tasks: Dialog State Tracking and Response Retrieval. Our experiments show that, in most setups, the best performance entails the combination of (I) conversational specialization in the target language and (ii) few-shot transfer for the concrete TOD task. Most importantly, we show that our conversational specialization in the target language allows for an exceptionally sample-efficient few-shot transfer for downstream TOD tasks.
Robust Task-Oriented Dialogue Generation with Contrastive Pre-training and Adversarial Filtering
Authors: Shiquan Yang, Xinting Huang, Jey Han Lau, Sarah Erfani
Date: 2022-05-20 00:00:00
Tasks: Contrastive Learning, Dialogue Generation

Data artifacts incentivize machine learning models to learn non-transferable generalizations by taking advantage of shortcuts in the data, and there is growing evidence that data artifacts play a role for the strong results that deep learning models achieve in recent natural language processing benchmarks. In this paper, we focus on task-oriented dialogue and investigate whether popular datasets such as MultiWOZ contain such data artifacts. We found that by only keeping frequent phrases in the training examples, state-of-the-art models perform similarly compared to the variant trained with full data, suggesting they exploit these spurious correlations to solve the task. Motivated by this, we propose a contrastive learning based framework to encourage the model to ignore these cues and focus on learning generalisable patterns. We also experiment with adversarial filtering to remove "easy" training instances so that the model would focus on learning from the "harder" instances. We conduct a number of generalization experiments -- e.g., cross-domain/dataset and adversarial tests -- to assess the robustness of our approach and found that it works exceptionally well.
Educational Tools for Mapuzugun
Authors: Cristian Ahumada, Claudio Gutierrez, Antonios Anastasopoulos
Date: 2022-05-19 00:00:00
Proceeding: NAACL (BEA) 2022 7

Mapuzugun is the language of the Mapuche people. Due to political and historical reasons, its number of speakers has decreased and the language has been excluded from the educational system in Chile and Argentina. For this reason, it is very important to support the revitalization of the Mapuzugun in all spaces and media of society. In this work we present a tool towards supporting educational activities of Mapuzugun, tailored to the characteristics of the language. The tool consists of three parts: design and development of an orthography detector and converter; a morphological analyzer; and an informal translator. We also present a case study with Mapuzugun students showing promising results. Short Abstract in Mapuzuzgun: T\"ufachi k\"uzaw pegelfi ki\~ne zugun k\"uzawpey\"um kelluaetew pu mapuzugun chillkatufe kimal kizu ta\~ni zugun.
Topic Segmentation of Research Article Collections
Authors: Erion Çano, Benjamin Roth
Date: 2022-05-18 00:00:00
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Segmentation, Text Summarization

Collections of research article data harvested from the web have become common recently since they are important resources for experimenting on tasks such as named entity recognition, text summarization, or keyword generation. In fact, certain types of experiments require collections that are both large and topically structured, with records assigned to separate research disciplines. Unfortunately, the current collections of publicly available research articles are either small or heterogeneous and unstructured. In this work, we perform topic segmentation of a paper data collection that we crawled and produce a multitopic dataset of roughly seven million paper data records. We construct a taxonomy of topics extracted from the data records and then annotate each document with its corresponding topic from that taxonomy. As a result, it is possible to use this newly proposed dataset in two modalities: as a heterogeneous collection of documents from various disciplines or as a set of homogeneous collections, each from a single research topic.
PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking
Authors: Yixuan Qiao, Hao Chen, Jun Wang, Tuozhen Liu, Xianbin Ye, Xin Tang, Rui Fang, Peng Gao, Wenfeng Xie, Guotong Xie
Date: 2022-05-18 00:00:00
Tasks: Deep Learning, General Knowledge, Retrieval

This paper describes the PASH participation in TREC 2021 Deep Learning Track. In the recall stage, we adopt a scheme combining sparse and dense retrieval method. In the multi-stage ranking phase, point-wise and pair-wise ranking strategies are used one after another based on model continual pre-trained on general knowledge and document-level data. Compared to TREC 2020 Deep Learning Track, we have additionally introduced the generative model T5 to further enhance the performance.
Robust Constrained Multi-objective Evolutionary Algorithm based on Polynomial Chaos Expansion for Trajectory Optimization
Authors: Yuji Takubo, Masahiro Kanazaki
Date: 2022-05-23 00:00:00
Tasks: Time Series, Time Series Analysis

An integrated optimization method based on the constrained multi-objective evolutionary algorithm (MOEA) and non-intrusive polynomial chaos expansion (PCE) is proposed, which solves robust multi-objective optimization problems under time-series dynamics. The constraints in such problems are difficult to handle, not only because the number of the dynamic constraints is multiplied by the discretized time steps but also because each of them is probabilistic. The proposed method rewrites a robust formulation into a deterministic problem via the PCE, and then sequentially processes the generated constraints in population generation, trajectory generation, and evaluation by the MOEA. As a case study, the landing trajectory design of supersonic transport (SST) with wind uncertainty is optimized. Results demonstrate the quantitative influence of the constraint values over the optimized solution sets and corresponding trajectories, proposing robust flight controls.
Probabilistic Structured Grammatical Evolution
Authors: Jessica Mégane, Nuno Lourenço, Penousal Machado
Date: 2022-05-21 00:00:00

The grammars used in grammar-based Genetic Programming (GP) methods have a significant impact on the quality of the solutions generated since they define the search space by restricting the solutions to its syntax. In this work, we propose Probabilistic Structured Grammatical Evolution (PSGE), a new approach that combines the Structured Grammatical Evolution (SGE) and Probabilistic Grammatical Evolution (PGE) representation variants and mapping mechanisms. The genotype is a set of dynamic lists, one for each non-terminal in the grammar, with each element of the list representing a probability used to select the next Probabilistic Context-Free Grammar (PCFG) derivation rule. PSGE statistically outperformed Grammatical Evolution (GE) on all six benchmark problems studied. In comparison to PGE, PSGE outperformed 4 of the 6 problems analyzed.
Probabilistic forecasting of German electricity imbalance prices
Authors: Michał Narajewski
Date: 2022-05-23 00:00:00

The exponential growth of renewable energy capacity has brought much uncertainty to electricity prices and to electricity generation. To address this challenge, the energy exchanges have been developing further trading possibilities, especially the intraday and balancing markets. For an energy trader participating in both markets, the forecasting of imbalance prices is of particular interest. Therefore, in this manuscript we conduct a very short-term probabilistic forecasting of imbalance prices, contributing to the scarce literature in this novel subject. The forecasting is performed 30 minutes before the delivery, so that the trader might still choose the trading place. The distribution of the imbalance prices is modelled and forecasted using methods well-known in the electricity price forecasting literature: lasso with bootstrap, gamlss, and probabilistic neural networks. The methods are compared with a naive benchmark in a meaningful rolling window study. The results provide evidence of the efficiency between the intraday and balancing markets as the sophisticated methods do not substantially overperform the intraday continuous price index. On the other hand, they significantly improve the empirical coverage. The analysis was conducted on the German market, however it could be easily applied to any other market of similar structure.
Decoupling multivariate functions using a nonparametric filtered tensor decomposition
Authors: Jan Decuyper, Koen Tiels, Siep Weiland, Mark C. Runacres, Johan Schoukens
Date: 2022-05-23 00:00:00
Tasks: Form, Tensor Decomposition

Multivariate functions emerge naturally in a wide variety of data-driven models. Popular choices are expressions in the form of basis expansions or neural networks. While highly effective, the resulting functions tend to be hard to interpret, in part because of the large number of required parameters. Decoupling techniques aim at providing an alternative representation of the nonlinearity. The so-called decoupled form is often a more efficient parameterisation of the relationship while being highly structured, favouring interpretability. In this work two new algorithms, based on filtered tensor decompositions of first order derivative information are introduced. The method returns nonparametric estimates of smooth decoupled functions. Direct applications are found in, i.a. the fields of nonlinear system identification and machine learning.
Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates
Authors: Jingfei Zhang, Yi Li
Date: 2022-05-21 00:00:00
Tasks: Multi-Task Learning, regression, Vocal Bursts Intensity Prediction

Gaussian graphical regression is a powerful means that regresses the precision matrix of a Gaussian graphical model on covariates, permitting the numbers of the response variables and covariates to far exceed the sample size. Model fitting is typically carried out via separate node-wise lasso regressions, ignoring the network-induced structure among these regressions. Consequently, the error rate is high, especially when the number of nodes is large. We propose a multi-task learning estimator for fitting Gaussian graphical regression models; we design a cross-task group sparsity penalty and a within task element-wise sparsity penalty, which govern the sparsity of active covariates and their effects on the graph, respectively. For computation, we consider an efficient augmented Lagrangian algorithm, which solves subproblems with a semi-smooth Newton method. For theory, we show that the error rate of the multi-task learning based estimates has much improvement over that of the separate node-wise lasso estimates, because the cross-task penalty borrows information across tasks. To address the main challenge that the tasks are entangled in a complicated correlation structure, we establish a new tail probability bound for correlated heavy-tailed (sub-exponential) variables with an arbitrary correlation structure, a useful theoretical result in its own right. Finally, the utility of our method is demonstrated through simulations as well as an application to a gene co-expression network study with brain cancer patients.
Designing Conversational Robots with Children during the Pandemic
Authors: Thomas Beelen, Ella Velner, Roeland Ordelman, Khiet P. Truong, Vanessa Evers, Theo Huibers
Date: 2022-05-23 00:00:00

Our research project (CHATTERS) is about designing a conversational robot for children's digital information search. We want to design a robot with a suitable conversation, that fosters a responsible trust relationship between child and robot. In this paper we give: 1) a preliminary view on an empirical study around children's trust in robots that provide information, which was conducted via video call due to the COVID-19 pandemic. 2) We also give a preliminary analysis of a co-design workshop we conducted, where the pandemic may have impacted children's design choices. (3) We close by describing the upcoming research activities we are developing.