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"Text": models, code, and papers

Character-based NMT with Transformer

Nov 12, 2019
Rohit Gupta, Laurent Besacier, Marc Dymetman, Matthias Gallé

Character-based translation has several appealing advantages, but its performance is in general worse than a carefully tuned BPE baseline. In this paper we study the impact of character-based input and output with the Transformer architecture. In particular, our experiments on EN-DE show that character-based Transformer models are more robust than their BPE counterpart, both when translating noisy text, and when translating text from a different domain. To obtain comparable BLEU scores in clean, in-domain data and close the gap with BPE-based models we use known techniques to train deeper Transformer models.


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Automatic Generation of Chinese Short Product Titles for Mobile Display

Nov 05, 2018
Yu Gong, Xusheng Luo, Kenny Q. Zhu, Wenwu Ou, Zhao Li, Lu Duan

This paper studies the problem of automatically extracting a short title from a manually written longer description of E-commerce products for display on mobile devices. It is a new extractive summarization problem on short text inputs, for which we propose a feature-enriched network model, combining three different categories of features in parallel. Experimental results show that our framework significantly outperforms several baselines by a substantial gain of 4.5%. Moreover, we produce an extractive summarization dataset for E-commerce short texts and will release it to the research community.

* IAAI 2019 

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Eval all, trust a few, do wrong to none: Comparing sentence generation models

Oct 30, 2018
Ondřej Cífka, Aliaksei Severyn, Enrique Alfonseca, Katja Filippova

In this paper, we study recent neural generative models for text generation related to variational autoencoders. Previous works have employed various techniques to control the prior distribution of the latent codes in these models, which is important for sampling performance, but little attention has been paid to reconstruction error. In our study, we follow a rigorous evaluation protocol using a large set of previously used and novel automatic and human evaluation metrics, applied to both generated samples and reconstructions. We hope that it will become the new evaluation standard when comparing neural generative models for text.

* 12 pages (3 page appendix); v2: added hyperparameter settings, clarifications 

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Stance Prediction for Russian: Data and Analysis

Oct 03, 2018
Nikita Lozhnikov, Leon Derczynski, Manuel Mazzara

Stance detection is a critical component of rumour and fake news identification. It involves the extraction of the stance a particular author takes related to a given claim, both expressed in text. This paper investigates stance classification for Russian. It introduces a new dataset, RuStance, of Russian tweets and news comments from multiple sources, covering multiple stories, as well as text classification approaches to stance detection as benchmarks over this data in this language. As well as presenting this openly-available dataset, the first of its kind for Russian, the paper presents a baseline for stance prediction in the language.


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A Formal Look at Dependency Grammars and Phrase-Structure Grammars, with Special Consideration of Word-Order Phenomena

Oct 18, 1994
Owen Rambow, Aravind Joshi

The central role of the lexicon in Meaning-Text Theory (MTT) and other dependency-based linguistic theories cannot be replicated in linguistic theories based on context-free grammars (CFGs). We describe Tree Adjoining Grammar (TAG) as a system that arises naturally in the process of lexicalizing CFGs. A TAG grammar can therefore be compared directly to an Meaning-Text Model (MTM). We illustrate this point by discussing the computational complexity of certain non-projective constructions, and suggest a way of incorporating locality of word-order definitions into the Surface-Syntactic Component of MTT.

* uuencoded compressed ps file, 20 pages 

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Automated Testing of AI Models

Oct 07, 2021
Swagatam Haldar, Deepak Vijaykeerthy, Diptikalyan Saha

The last decade has seen tremendous progress in AI technology and applications. With such widespread adoption, ensuring the reliability of the AI models is crucial. In past, we took the first step of creating a testing framework called AITEST for metamorphic properties such as fairness, robustness properties for tabular, time-series, and text classification models. In this paper, we extend the capability of the AITEST tool to include the testing techniques for Image and Speech-to-text models along with interpretability testing for tabular models. These novel extensions make AITEST a comprehensive framework for testing AI models.

* 5 pages, 3 Figures, 4 Tables 

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BERT: A Review of Applications in Natural Language Processing and Understanding

Mar 22, 2021
M. V. Koroteev

In this review, we describe the application of one of the most popular deep learning-based language models - BERT. The paper describes the mechanism of operation of this model, the main areas of its application to the tasks of text analytics, comparisons with similar models in each task, as well as a description of some proprietary models. In preparing this review, the data of several dozen original scientific articles published over the past few years, which attracted the most attention in the scientific community, were systematized. This survey will be useful to all students and researchers who want to get acquainted with the latest advances in the field of natural language text analysis.

* 18 pages, 7 figures 

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Is this sentence valid? An Arabic Dataset for Commonsense Validation

Aug 25, 2020
Saja Tawalbeh, Mohammad AL-Smadi

The commonsense understanding and validation remains a challenging task in the field of natural language understanding. Therefore, several research papers have been published that studied the capability of proposed systems to evaluate the models ability to validate commonsense in text. In this paper, we present a benchmark Arabic dataset for commonsense understanding and validation as well as a baseline research and models trained using the same dataset. To the best of our knowledge, this dataset is considered as the first in the field of Arabic text commonsense validation. The dataset is distributed under the Creative Commons BY-SA 4.0 license and can be found on GitHub.

* 4 pages 

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Exploring Hate Speech Detection in Multimodal Publications

Oct 09, 2019
Raul Gomez, Jaume Gibert, Lluis Gomez, Dimosthenis Karatzas

In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research.


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Automated Chess Commentator Powered by Neural Chess Engine

Sep 23, 2019
Hongyu Zang, Zhiwei Yu, Xiaojun Wan

In this paper, we explore a new approach for automated chess commentary generation, which aims to generate chess commentary texts in different categories (e.g., description, comparison, planning, etc.). We introduce a neural chess engine into text generation models to help with encoding boards, predicting moves, and analyzing situations. By jointly training the neural chess engine and the generation models for different categories, the models become more effective. We conduct experiments on 5 categories in a benchmark Chess Commentary dataset and achieve inspiring results in both automatic and human evaluations.

* The first two authors contributed equally to this paper 

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