The Impact of Indirect Machine Translation on Sentiment Classification
ADAPT Centre, Dublin City University（都柏林城市大学 ADAPT 中心）；Trinity Centre for Literary and Cultural Translation and ADAPT Centre, Trinity College Dublin（都柏林圣三一学院文学与文化翻译中心及 ADAPT 中心）
Alberto Poncelas, Pintu Lohar, Andy Way, James Hadley
发表信息：
Proceedings of Association for Machine Translation in the Americas, AMTA (2020)
Sentiment classification has been crucial for many natural language processing (NLP) applications, such as the analysis of movie reviews, tweets, or customer feedback. A sufficiently large amount of data is required to build a robust sentiment classification system. However, such resources are not always available for all domains or for all languages.
In this work, we propose employing a machine translation (MT) system to translate customer feedback into another language to investigate in which cases translated sentences can have a positive or negative impact on an automatic sentiment classifier. Furthermore, as performing a direct translation is not always possible, we explore the performance of automatic classifiers on sentences that have been translated using a pivot MT system.
We conduct several experiments using the above approaches to analyse the performance of our proposed sentiment classification system and discuss the advantages and drawbacks of classifying translated sentences.
In the current work, we explore the enrichment in the machine translation output when the training parallel corpus is augmented with the introduction of sentiment analysis. The paper discusses the preparation of the same sentiment tagged English-Bengali parallel corpus. The preparation of raw parallel corpus, sentiment analysis of the sentences and the training of a Character Based Neural Machine Translation model using the same has been discussed extensively in this paper. The output of the translation model has been compared with a base-line translation model using automated metrics such as …
University of Wolverhampton（伍尔弗汉普顿大学）；University of Surrey（萨里大学）；De Montfort University（德蒙福特大学）
In translating text where sentiment is the main message, human translators give particular attention to sentiment-carrying words. The reason is that an incorrect translation of such words would miss the fundamental aspect of the source text, i.e. the author's sentiment. In the online world, MT systems are extensively used to translate User-Generated Content (UGC) such as reviews, tweets, and social media posts, where the main message is often the author's positive or negative attitude towards the topic of the text. It is important in such scenarios to accurately measure how far an MT system ca…
University of Surrey, UK（萨里大学（英国））；Centre for Translation Studies（翻译研究中心）
Since the advent of Neural Machine Translation (NMT) approaches there has been a tremendous improvement in the quality of automatic translation. However, NMT output still lacks accuracy in some low-resource languages and sometimes makes major errors that need extensive post-editing. This is particularly noticeable with texts that do not follow common lexico-grammatical standards, such as user generated content (UGC). In this paper we investigate the challenges involved in translating book reviews from Arabic into English, with particular focus on the errors that lead to incorrect translation o…
The University of Melbourne（墨尔本大学）；Andalas University（安达拉斯大学）
Although some linguists (Rusmali et al., 1985; Crouch, 2009) have fairly attempted to define the morphology and syntax of Minangkabau, information processing in this language is still absent due to the scarcity of the annotated resource. In this work, we release two Minangkabau corpora: sentiment analysis and machine translation that are harvested and constructed from Twitter and Wikipedia. We conduct the first computational linguistics in Minangkabau language employing classic machine learning and sequence-to-sequence models such as LSTM and Transformer. Our first experiments show that the cl…
The explosion of user-generated content (UGC)--e.g. social media posts, comments, and reviews--has motivated the development of NLP applications tailored to these types of informal texts. Prevalent among these applications have been sentiment analysis and machine translation (MT). Grounded in the observation that UGC features highly idiomatic, sentiment-charged language, we propose a decoder-side approach that incorporates automatic sentiment scoring into the MT candidate selection process. We train separate English and Spanish sentiment classifiers, then, using n-best candidates generated by …
LACA: Improving Cross-lingual Aspect-Based Sentiment Analysis with LLM Data Augmentation
Department of Computer Science and Engineering（计算机科学与工程系）；NTIS – New Technologies for the Information Society University of West Bohemia in Pilsen, Faculty of Applied Sciences（新信息技术社会大学西波希米亚大学皮尔森分校应用科学学院）
Contribution au Niveau de l'Approche Indirecte à Base de Transfert dans la Traduction Automatique
UNIVERSITE FERHAT ABBAS – SETIF 1（费哈特·阿巴斯大学-塞提夫1号）；Faculté des Sciences（理学院）；Université El Oued（乌埃勒大学）；Université Sétif -1-（塞提夫1号大学）；Université Souk Ahras（苏克阿赫拉斯大学）；Université BBA（巴特纳大学）
Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding
Department of Computer Science and Engineering, University of West Bohemia in Pilsen（西波希米亚大学皮尔森分校计算机科学与工程系）；NTIS - New Technologies for the Information Society, University of West Bohemia in Pilsen（信息社会新技术机构，西波希米亚大学皮尔森分校）
Generative Models Enhanced by Sequence Labelling and Aspect-Code Switching Improve Cross-lingual Aspect-Based Sentiment Analysis
University of West Bohemia in Pilsen（比尔森西波希米亚大学）；NTIS – New Technologies for the Information Society（NTIS——信息社会新技术研究院）；Faculty of Applied Sciences（应用科学学院）
Big languages such as English and Finnish have many natural language processing (NLP) resources and models, but this is not the case for low-resourced and endangered languages as such resources are so scarce despite the great advantages they would provide for the language communities. The most common types of resources available for low-resourced and endangered languages are translation dictionaries and universal dependencies. In this paper, we present a method for constructing word embeddings for endangered languages using existing word embeddings of different resource-rich languages and the …
Multimodal machine translation and textual chat translation have received considerable attention in recent years. Although the conversation in its natural form is usually multimodal, there still lacks work on multimodal machine translation in conversations. In this work, we introduce a new task named Multimodal Chat Translation (MCT), aiming to generate more accurate translations with the help of the associated dialogue history and visual context. To this end, we firstly construct a Multimodal Sentiment Chat Translation Dataset (MSCTD) containing 142,871 English-Chinese utterance pairs in 14,7…
Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis
Media Informatics Group, University of Regensburg, Regensburg, Germany（莱茵河畔大学媒体信息学小组）；Information Science Group, University of Regensburg, Regensburg, Germany（莱茵河畔大学信息科学小组）
TALP Research Center（TALP研究中心）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；IXA NLP Group（IXA自然语言处理组）；University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））
We propose a modular architecture of language-specific encoder-decoders that constitutes a multilingual machine translation system that can be incrementally extended to new languages without the need for retraining the existing system when adding new languages. Differently from previous works, we simultaneously train $N$ languages in all translation directions by alternately freezing encoder or decoder modules, which indirectly forces the system to train in a common intermediate representation for all languages. Experimental results from multilingual machine translation show that we can succes…
University of Wolverhampton（伍尔弗汉普顿大学）；University of Surrey（萨里大学）
Social media companies as well as authorities make extensive use of artificial intelligence (AI) tools to monitor postings of hate speech, celebrations of violence or profanity. Since AI software requires massive volumes of data to train computers, Machine Translation (MT) of the online content is commonly used to process posts written in several languages and hence augment the data needed for training. However, MT mistakes are a regular occurrence when translating sentiment-oriented user-generated content (UGC), especially when a low-resource language is involved. The adequacy of the whole pr…
Resolution of Indirect Anaphora in Japanese Sentences Using Examples 'X no Y (Y of X)'
名词短语可以间接指代已经提到的实体。例如，“我昨晚进了一所老房子。屋顶漏水很严重... ''表示''屋顶''与''老房子}''相关联，这在前一句中已经提到。这类照应（间接回指）在自然语言处理中尚未得到很好的研究，但对连贯性解析、语言理解和机器翻译都很重要。为了分析间接回指，我们需要一个包含两个名词之间关系知识的名词格框架词典，但目前还没有这样的词典。因此，我们被迫使用“X no Y”（X的Y）和动词格框架词典的例子。我们尝试使用这些信息来估计间接回指，在测试句子上获得了63%的召回率和68%的准确率。这表明，当我们不能使用名词格框架词典时，“X无Y”信息在一定程度上是有用的。我们对名词格框架词典的结果进行了估计，得到了71%的召回率和82%的准确率。最后，我们提出了一种以``X no Y为例构造名词格框架词典的方法。''
Tweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to find huge annotated corpora for non-English languages. The recent rise of the transformer models in Natural Language Processing allows to achieve unparalleled performances in many tasks, but these models need a consequent quantity of text to adapt to the tweet domain. We propose the use of a multilingual transformer model, that we pre-train over English tweets and apply data-augmentation using automatic translation…
In social-media platforms such as Twitter, Facebook, and Reddit, people prefer to use code-mixed language such as Spanish-English, Hindi-English to express their opinions. In this paper, we describe different models we used, using the external dataset to train embeddings, ensembling methods for Sentimix, and OffensEval tasks. The use of pre-trained embeddings usually helps in multiple tasks such as sentence classification, and machine translation. In this experiment, we haveused our trained code-mixed embeddings and twitter pre-trained embeddings to SemEval tasks. We evaluate our models on mac…
Multimodal sentiment analysis is an important research area that predicts speaker's sentiment tendency through features extracted from textual, visual and acoustic modalities. The central challenge is the fusion method of the multimodal information. A variety of fusion methods have been proposed, but few of them adopt end-to-end translation models to mine the subtle correlation between modalities. Enlightened by recent success of Transformer in the area of machine translation, we propose a new fusion method, TransModality, to address the task of multimodal sentiment analysis. We assume that tr…
English is the international standard of social research, but scholars are increasingly conscious of their responsibility to meet the need for scholarly insight into communication processes globally. This tension is as true in computational methods as any other area, with revolutionary advances in the tools for English language texts leaving most other languages far behind. In this paper, we aim to leverage those very advances to demonstrate that multi-language analysis is currently accessible to all computational scholars. We show that English-trained measures computed after translation to En…
Multimodal target/aspect sentiment classification combines multimodal sentiment analysis and aspect/target sentiment classification. The goal of the task is to combine vision and language to understand the sentiment towards a target entity in a sentence. Twitter is an ideal setting for the task because it is inherently multimodal, highly emotional, and affects real world events. However, multimodal tweets are short and accompanied by complex, possibly irrelevant images. We introduce a two-stream model that translates images in input space using an object-aware transformer followed by a single-…
Evaluation of Google Translate for Mandarin Chinese translation using sentiment and semantic analysis
School of Mathematics and Statistics, UNSW Sydney（新南威尔士大学悉尼分校数学与统计学院）；Pingala Institute（平加拉研究所）；Centre for Artificial Intelligence and Innovation（人工智能与创新中心）
An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses
Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, UNSW Sydney（过渡人工智能研究组，数学与统计学学院，UNSW悉尼）；Department of Civil Engineering, National Institute of Technology, Rourkela, India（土木工程系，印度国家理工学院，拉尔凯拉）；Centre for Artificial Intelligence and Innovation, Pingla Institute, Sydney, Australia（人工智能与创新中心，Pingla研究所，悉尼，澳大利亚）
University of New South Wales（新南威尔士大学）；Indian Institute of Technology Guwahati（印度理工学院古瓦哈蒂分校）
It is well known that translations of songs and poems not only break rhythm and rhyming patterns, but can also result in loss of semantic information. The Bhagavad Gita is an ancient Hindu philosophical text originally written in Sanskrit that features a conversation between Lord Krishna and Arjuna prior to the Mahabharata war. The Bhagavad Gita is also one of the key sacred texts in Hinduism and is known as the forefront of the Vedic corpus of Hinduism. In the last two centuries, there has been a lot of interest in Hindu philosophy from western scholars; hence, the Bhagavad Gita has been tran…
We evaluate a simple approach to improving zero-shot multilingual transfer of mBERT on social media corpus by adding a pretraining task called translation pair prediction (TPP), which predicts whether a pair of cross-lingual texts are a valid translation. Our approach assumes access to translations (exact or approximate) between source-target language pairs, where we fine-tune a model on source language task data and evaluate the model in the target language. In particular, we focus on language pairs where transfer learning is difficult for mBERT: those where source and target languages are di…
