pwc-archive/papers-with-abstracts huggingface dataset:
Between Flexibility and Consistency: Joint Generation of Captions and Subtitles
Authors: Alina Karakanta, Marco Gaido, Matteo Negri, Marco Turchi
Date: 2021-07-13 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Translation

Speech translation (ST) has lately received growing interest for the generation of subtitles without the need for an intermediate source language transcription and timing (i.e. captions). However, the joint generation of source captions and target subtitles does not only bring potential output quality advantages when the two decoding processes inform each other, but it is also often required in multilingual scenarios. In this work, we focus on ST models which generate consistent captions-subtitles in terms of structure and lexical content. We further introduce new metrics for evaluating subtitling consistency. Our findings show that joint decoding leads to increased performance and consistency between the generated captions and subtitles while still allowing for sufficient flexibility to produce subtitles conforming to language-specific needs and norms.
The IWSLT 2021 BUT Speech Translation Systems
Authors: Hari Krishna Vydana, Martin Karafi'at, Luk'as Burget, "Honza" Cernock'y
Date: 2021-07-13 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Machine Translation, speech-recognition, Speech Recognition, Translation

The paper describes BUT's English to German offline speech translation(ST) systems developed for IWSLT2021. They are based on jointly trained Automatic Speech Recognition-Machine Translation models. Their performances is evaluated on MustC-Common test set. In this work, we study their efficiency from the perspective of having a large amount of separate ASR training data and MT training data, and a smaller amount of speech-translation training data. Large amounts of ASR and MT training data are utilized for pre-training the ASR and MT models. Speech-translation data is used to jointly optimize ASR-MT models by defining an end-to-end differentiable path from speech to translations. For this purpose, we use the internal continuous representations from the ASR-decoder as the input to MT module. We show that speech translation can be further improved by training the ASR-decoder jointly with the MT-module using large amount of text-only MT training data. We also show significant improvements by training an ASR module capable of generating punctuated text, rather than leaving the punctuation task to the MT module.
On the Difficulty of Translating Free-Order Case-Marking Languages
Authors: Arianna Bisazza, Ahmet Üstün, Stephan Sportel
Date: 2021-07-13 00:00:00
Tasks: Machine Translation, NMT, Translation

Identifying factors that make certain languages harder to model than others is essential to reach language equality in future Natural Language Processing technologies. Free-order case-marking languages, such as Russian, Latin or Tamil, have proved more challenging than fixed-order languages for the tasks of syntactic parsing and subject-verb agreement prediction. In this work, we investigate whether this class of languages is also more difficult to translate by state-of-the-art Neural Machine Translation models (NMT). Using a variety of synthetic languages and a newly introduced translation challenge set, we find that word order flexibility in the source language only leads to a very small loss of NMT quality, even though the core verb arguments become impossible to disambiguate in sentences without semantic cues. The latter issue is indeed solved by the addition of case marking. However, in medium- and low-resource settings, the overall NMT quality of fixed-order languages remains unmatched.
Rating Facts under Coarse-to-fine Regimes
Authors: Guojun Wu
Date: 2021-07-13 00:00:00
Tasks: Classification, Fact Checking

The rise of manipulating fake news as a political weapon has become a global concern and highlighted the incapability of manually fact checking against rapidly produced fake news. Thus, statistical approaches are required if we are to address this problem efficiently. The shortage of publicly available datasets is one major bottleneck of automated fact checking. To remedy this, we collected 24K manually rated statements from PolitiFact. The class values exhibit a natural order with respect to truthfulness as shown in Table 1. Thus, our task represents a twist from standard classification, due to the various degrees of similarity between classes. To investigate this, we defined coarse-to-fine classification regimes, which presents new challenge for classification. To address this, we propose BERT-based models. After training, class similarity is sensible over the multi-class datasets, especially in the fine-grained one. Under all the regimes, BERT achieves state of the art, while the additional layers provide insignificant improvement.
Zero-shot Speech Translation
Authors: Tu Anh Dinh
Date: 2021-07-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, Translation

Speech Translation (ST) is the task of translating speech in one language into text in another language. Traditional cascaded approaches for ST, using Automatic Speech Recognition (ASR) and Machine Translation (MT) systems, are prone to error propagation. End-to-end approaches use only one system to avoid propagating error, yet are difficult to employ due to data scarcity. We explore zero-shot translation, which enables translating a pair of languages that is unseen during training, thus avoid the use of end-to-end ST data. Zero-shot translation has been shown to work for multilingual machine translation, yet has not been studied for speech translation. We attempt to build zero-shot ST models that are trained only on ASR and MT tasks but can do ST task during inference. The challenge is that the representation of text and audio is significantly different, thus the models learn ASR and MT tasks in different ways, making it non-trivial to perform zero-shot. These models tend to output the wrong language when performing zero-shot ST. We tackle the issues by including additional training data and an auxiliary loss function that minimizes the text-audio difference. Our experiment results and analysis show that the methods are promising for zero-shot ST. Moreover, our methods are particularly useful in the few-shot settings where a limited amount of ST data is available, with improvements of up to +11.8 BLEU points compared to direct end-to-end ST models and +3.9 BLEU points compared to ST models fine-tuned from pre-trained ASR model.
Generating Gender Augmented Data for NLP
Authors: Nishtha Jain, Maja Popovic, Declan Groves, Eva Vanmassenhove
Date: 2021-07-13 00:00:00
Proceeding: ACL (GeBNLP) 2021 8
Tasks: Machine Translation, NMT, Sentence, Translation

Gender bias is a frequent occurrence in NLP-based applications, especially pronounced in gender-inflected languages. Bias can appear through associations of certain adjectives and animate nouns with the natural gender of referents, but also due to unbalanced grammatical gender frequencies of inflected words. This type of bias becomes more evident in generating conversational utterances where gender is not specified within the sentence, because most current NLP applications still work on a sentence-level context. As a step towards more inclusive NLP, this paper proposes an automatic and generalisable rewriting approach for short conversational sentences. The rewriting method can be applied to sentences that, without extra-sentential context, have multiple equivalent alternatives in terms of gender. The method can be applied both for creating gender balanced outputs as well as for creating gender balanced training data. The proposed approach is based on a neural machine translation (NMT) system trained to 'translate' from one gender alternative to another. Both the automatic and manual analysis of the approach show promising results for automatic generation of gender alternatives for conversational sentences in Spanish.
Conformer-based End-to-end Speech Recognition With Rotary Position Embedding
Authors: Shengqiang Li, Menglong Xu, Xiao-Lei Zhang
Date: 2021-07-13 00:00:00
Tasks: Position, speech-recognition, Speech Recognition

Transformer-based end-to-end speech recognition models have received considerable attention in recent years due to their high training speed and ability to model a long-range global context. Position embedding in the transformer architecture is indispensable because it provides supervision for dependency modeling between elements at different positions in the input sequence. To make use of the time order of the input sequence, many works inject some information about the relative or absolute position of the element into the input sequence. In this work, we investigate various position embedding methods in the convolution-augmented transformer (conformer) and adopt a novel implementation named rotary position embedding (RoPE). RoPE encodes absolute positional information into the input sequence by a rotation matrix, and then naturally incorporates explicit relative position information into a self-attention module. To evaluate the effectiveness of the RoPE method, we conducted experiments on AISHELL-1 and LibriSpeech corpora. Results show that the conformer enhanced with RoPE achieves superior performance in the speech recognition task. Specifically, our model achieves a relative word error rate reduction of 8.70% and 7.27% over the conformer on test-clean and test-other sets of the LibriSpeech corpus respectively.
A Configurable Multilingual Model is All You Need to Recognize All Languages
Authors: Long Zhou, Jinyu Li, Eric Sun, Shujie Liu
Date: 2021-07-13 00:00:00
Tasks: All, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Multilingual automatic speech recognition (ASR) models have shown great promise in recent years because of the simplified model training and deployment process. Conventional methods either train a universal multilingual model without taking any language information or with a 1-hot language ID (LID) vector to guide the recognition of the target language. In practice, the user can be prompted to pre-select several languages he/she can speak. The multilingual model without LID cannot well utilize the language information set by the user while the multilingual model with LID can only handle one pre-selected language. In this paper, we propose a novel configurable multilingual model (CMM) which is trained only once but can be configured as different models based on users' choices by extracting language-specific modules together with a universal model from the trained CMM. Particularly, a single CMM can be deployed to any user scenario where the users can pre-select any combination of languages. Trained with 75K hours of transcribed anonymized Microsoft multilingual data and evaluated with 10-language test sets, the proposed CMM improves from the universal multilingual model by 26.0%, 16.9%, and 10.4% relative word error reduction when the user selects 1, 2, or 3 languages, respectively. CMM also performs significantly better on code-switching test sets.
A Dialogue-based Information Extraction System for Medical Insurance Assessment
Authors: Shuang Peng, Mengdi Zhou, Minghui Yang, Haitao Mi, Shaosheng Cao, Zujie Wen, Teng Xu, Hongbin Wang, Lei Liu
Date: 2021-07-13 00:00:00
Proceeding: Findings (ACL) 2021 8

In the Chinese medical insurance industry, the assessor's role is essential and requires significant efforts to converse with the claimant. This is a highly professional job that involves many parts, such as identifying personal information, collecting related evidence, and making a final insurance report. Due to the coronavirus (COVID-19) pandemic, the previous offline insurance assessment has to be conducted online. However, for the junior assessor often lacking practical experience, it is not easy to quickly handle such a complex online procedure, yet this is important as the insurance company needs to decide how much compensation the claimant should receive based on the assessor's feedback. In order to promote assessors' work efficiency and speed up the overall procedure, in this paper, we propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment. With the assistance of our system, the average time cost of the procedure is reduced from 55 minutes to 35 minutes, and the total human resources cost is saved 30% compared with the previous offline procedure. Until now, the system has already served thousands of online claim cases.
Enforcing Consistency in Weakly Supervised Semantic Parsing
Authors: Nitish Gupta, Sameer Singh, Matt Gardner
Date: 2021-07-13 00:00:00
Proceeding: ACL 2021 5
Tasks: Semantic Parsing, Visual Reasoning

The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons. Prior work uses elaborate search strategies to mitigate the prevalence of spurious programs; however, they typically consider only one input at a time. In this work we explore the use of consistency between the output programs for related inputs to reduce the impact of spurious programs. We bias the program search (and thus the model's training signal) towards programs that map the same phrase in related inputs to the same sub-parts in their respective programs. Additionally, we study the importance of designing logical formalisms that facilitate this kind of consAistency-based training. We find that a more consistent formalism leads to improved model performance even without consistency-based training. When combined together, these two insights lead to a 10% absolute improvement over the best prior result on the Natural Language Visual Reasoning dataset.
Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task
Authors: Yun Tang, Juan Pino, Xian Li, Changhan Wang, Dmitriy Genzel
Date: 2021-07-12 00:00:00
Proceeding: ACL 2021 5
Tasks: Decoder, Knowledge Distillation, Speech-to-Text, Speech-to-Text Translation, Transfer Learning, Translation

Pretraining and multitask learning are widely used to improve the speech to text translation performance. In this study, we are interested in training a speech to text translation model along with an auxiliary text to text translation task. We conduct a detailed analysis to understand the impact of the auxiliary task on the primary task within the multitask learning framework. Our analysis confirms that multitask learning tends to generate similar decoder representations from different modalities and preserve more information from the pretrained text translation modules. We observe minimal negative transfer effect between the two tasks and sharing more parameters is helpful to transfer knowledge from the text task to the speech task. The analysis also reveals that the modality representation difference at the top decoder layers is still not negligible, and those layers are critical for the translation quality. Inspired by these findings, we propose three methods to improve translation quality. First, a parameter sharing and initialization strategy is proposed to enhance information sharing between the tasks. Second, a novel attention-based regularization is proposed for the encoders and pulls the representations from different modalities closer. Third, an online knowledge distillation is proposed to enhance the knowledge transfer from the text to the speech task. Our experiments show that the proposed approach improves translation performance by more than 2 BLEU over a strong baseline and achieves state-of-the-art results on the \textsc{MuST-C} English-German, English-French and English-Spanish language pairs.
Few-shot Language Coordination by Modeling Theory of Mind
Authors: Hao Zhu, Graham Neubig, Yonatan Bisk
Date: 2021-07-12 00:00:00

$\textit{No man is an island.}$ Humans communicate with a large community by coordinating with different interlocutors within short conversations. This ability has been understudied by the research on building neural communicative agents. We study the task of few-shot $\textit{language coordination}$: agents quickly adapting to their conversational partners' language abilities. Different from current communicative agents trained with self-play, we require the lead agent to coordinate with a $\textit{population}$ of agents with different linguistic abilities, quickly adapting to communicate with unseen agents in the population. This requires the ability to model the partner's beliefs, a vital component of human communication. Drawing inspiration from theory-of-mind (ToM; Premack& Woodruff (1978)), we study the effect of the speaker explicitly modeling the listeners' mental states. The speakers, as shown in our experiments, acquire the ability to predict the reactions of their partner, which helps it generate instructions that concisely express its communicative goal. We examine our hypothesis that the instructions generated with ToM modeling yield better communication performance in both a referential game and a language navigation task. Positive results from our experiments hint at the importance of explicitly modeling communication as a socio-pragmatic progress.
Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation
Authors: Evan Williams, Paul Rodrigues, Sieu Tran
Date: 2021-07-12 00:00:00
Tasks: Data Augmentation

This paper discusses the approach used by the Accenture Team for CLEF2021 CheckThat! Lab, Task 1, to identify whether a claim made in social media would be interesting to a wide audience and should be fact-checked. Twitter training and test data were provided in English, Arabic, Spanish, Turkish, and Bulgarian. Claims were to be classified (check-worthy/not check-worthy) and ranked in priority order for the fact-checker. Our method used deep neural network transformer models with contextually sensitive lexical augmentation applied on the supplied training datasets to create additional training samples. This augmentation approach improved the performance for all languages. Overall, our architecture and data augmentation pipeline produced the best submitted system for Arabic, and performance scales according to the quantity of provided training data for English, Spanish, Turkish, and Bulgarian. This paper investigates the deep neural network architectures for each language as well as the provided data to examine why the approach worked so effectively for Arabic, and discusses additional data augmentation measures that should could be useful to this problem.
Identification of Dynamical Systems using Symbolic Regression
Authors: Gabriel Kronberger, Lukas Kammerer, Michael Kommenda
Date: 2021-07-06 00:00:00
Tasks: regression, Symbolic Regression

We describe a method for the identification of models for dynamical systems from observational data. The method is based on the concept of symbolic regression and uses genetic programming to evolve a system of ordinary differential equations (ODE). The novelty is that we add a step of gradient-based optimization of the ODE parameters. For this we calculate the sensitivities of the solution to the initial value problem (IVP) using automatic differentiation. The proposed approach is tested on a set of 19 problem instances taken from the literature which includes datasets from simulated systems as well as datasets captured from mechanical systems. We find that gradient-based optimization of parameters improves predictive accuracy of the models. The best results are obtained when we first fit the individual equations to the numeric differences and then subsequently fine-tune the identified parameter values by fitting the IVP solution to the observed variable values.
Robust Learning of Optimal Auctions
Authors: Wenshuo Guo, Michael I. Jordan, Manolis Zampetakis
Date: 2021-07-13 00:00:00
Proceeding: NeurIPS 2021 12

We study the problem of learning revenue-optimal multi-bidder auctions from samples when the samples of bidders' valuations can be adversarially corrupted or drawn from distributions that are adversarially perturbed. First, we prove tight upper bounds on the revenue we can obtain with a corrupted distribution under a population model, for both regular valuation distributions and distributions with monotone hazard rate (MHR). We then propose new algorithms that, given only an ``approximate distribution'' for the bidder's valuation, can learn a mechanism whose revenue is nearly optimal simultaneously for all ``true distributions'' that are $\alpha$-close to the original distribution in Kolmogorov-Smirnov distance. The proposed algorithms operate beyond the setting of bounded distributions that have been studied in prior works, and are guaranteed to obtain a fraction $1-O(\alpha)$ of the optimal revenue under the true distribution when the distributions are MHR. Moreover, they are guaranteed to yield at least a fraction $1-O(\sqrt{\alpha})$ of the optimal revenue when the distributions are regular. We prove that these upper bounds cannot be further improved, by providing matching lower bounds. Lastly, we derive sample complexity upper bounds for learning a near-optimal auction for both MHR and regular distributions.
Parameter selection in Gaussian process interpolation: an empirical study of selection criteria
Authors: Sébastien Petit, Julien Bect, Paul Feliot, Emmanuel Vazquez
Date: 2021-07-13 00:00:00

This article revisits the fundamental problem of parameter selection for Gaussian process interpolation. By choosing the mean and the covariance functions of a Gaussian process within parametric families, the user obtains a family of Bayesian procedures to perform predictions about the unknown function, and must choose a member of the family that will hopefully provide good predictive performances. We base our study on the general concept of scoring rules, which provides an effective framework for building leave-one-out selection and validation criteria, and a notion of extended likelihood criteria based on an idea proposed by Fasshauer and co-authors in 2009, which makes it possible to recover standard selection criteria such as, for instance, the generalized cross-validation criterion. Under this setting, we empirically show on several test problems of the literature that the choice of an appropriate family of models is often more important than the choice of a particular selection criterion (e.g., the likelihood versus a leave-one-out selection criterion). Moreover, our numerical results show that the regularity parameter of a Mat{\'e}rn covariance can be selected effectively by most selection criteria.