Dominican Republic

Existing approaches for audio-language task- specific prediction focus on building compli- cated late-fusion mechanisms. However, these models face challenges of overfitting with lim- ited labels and poor generalization. In this pa- per, we present a Cross-modal Transformer for Audio-and-Language, i.e., CTAL, which aims to learn the intra- and inter- modali- ties connections between audio and language through two proxy tasks from a large num- ber of audio-and-language pairs: masked language modeling and masked cross-modal acoustic modeling. After fine-tuning our CTAL model on multiple downstream audio- and-language tasks, we observe significant improvements on different tasks, including emotion classification, sentiment analysis, and speaker verification. Furthermore, we design a fusion mechanism in the fine-tuning phase, which allows CTAL to achieve better perfor- mance. Lastly, we conduct detailed ablation studies to demonstrate that both our novel cross-modality fusion component and audio- language pre-training methods contribute to the promising results.

EMNLP 2021

CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

<iframe style="width:700px;height:400px" src="https://online.fliphtml5.com/ebtyf/ceby/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe>

Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

poster

Opinion Role Labeling (ORL), aiming to identify the key roles of opinion, has received increasing interest. Unlike most of the previous works focusing on the English language, in this paper, we present the first work of Chinese ORL. We construct a Chinese dataset by manually translating and projecting annotations from a standard English MPQA dataset. Then, we investigate the effectiveness of cross-lingual transfer methods, including model transfer and corpus translation. We exploit multilingual BERT with Contextual Parameter Generator and Adapter methods to examine the potentials of unsupervised cross-lingual learning and our experiments and analyses for both bilingual and multilingual transfers establish a foundation for the future research of this task.

Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study

Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in an input sentence, which is significantly important in real-world applications. To perform ABSA, one faces a long-standing challenge that ABSA requires the model to have a good understanding of the contextual information, especially the particular patterns that suggests the sentiment polarity. However, these patterns vary in different sentences, especially when the sentences come from different sources (domains). Although leveraging extra labeled data from different sources (domains) is always a promising solution to address the challenge, in practical applications, these labeled data are always stored at different locations and might be inaccessible to each other due to privacy or legal concerns (e.g., the data are owned by different companies). To address the issue and benefit from extra labeled data, we propose a novel ABSA model with federated learning (FL) adopted to overcome the data isolation limitations and topic memory (TM) proposed to enhance FL by accommodating the cases of data from diverse sources (domains). Particularly, TM contributes to overcoming difficulties in identifying domains due to data inaccessibility by providing useful categorical information for localized predictions. Experimental results on a simulated environment for FL with three nodes demonstrate the effectiveness of our approach, where TM-FL outperforms different baselines including some well-designed FL frameworks.

Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories

As an important task in opinion mining, comparative opinion mining aims to identify comparative sentences from product reviews, extract the comparative elements, and obtain the corresponding comparative opinion tuples. However, most previous studies simply regarded comparative tuple extraction as comparative element extraction, but ignored the fact that many comparative sentences may contain multiple comparisons. The comparative opinion tuples defined in these studies also failed to explicitly provide comparative preferences. To address these limitations, in this work we first introduce a new Comparative Opinion Quintuple Extraction (COQE) task, to identify comparative sentences from product reviews and extract all comparative opinion quintuples (Subject, Object, Comparative Aspect, Comparative Opinion, Comparative Preference). Secondly, based on the existing comparative opinion mining corpora, we make supplementary annotations and construct three datasets for the COQE task. Finally, we benchmark the COQE task by proposing a new BERT-based multi-stage approach as well as three baseline systems extended from previous methods. Experimental results show that the new approach significantly outperforms three baseline systems on three datasets for the COQE task.

Comparative Opinion Quintuple Extraction from Product Reviews

Implicit sentiment analysis, aiming at detecting the sentiment of a sentence without sentiment words, has become an attractive research topic in recent years. In this paper, we focus on event-centric implicit sentiment analysis that utilizes the sentiment-aware event contained in a sentence to infer its sentiment polarity. Most existing methods in implicit sentiment analysis simply view noun phrases or entities in text as events or indirectly model events with sophisticated models. Since events often trigger sentiments in sentences, we argue that this task would beneﬁt from explicit modeling of events and event representation learning. To this end, we represent an event as the combination of its event type and the event triplet <subject, predicate, object>. Based on such event representation, we further propose a novel model with hierarchical tensor-based composition mechanism to detect sentiment in text. In addition, we present a dataset for event-centric implicit sentiment analysis where each sentence is labeled with the event representation described above. Experimental results on our constructed dataset and an existing benchmark dataset show the effectiveness of the proposed approach.

Implicit Sentiment Analysis with Event-centered Text Representation

Data augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification. However, existing studies aimed to improve the generalization ability by augmenting the training data with synonymous examples or adding random noises to word embeddings, which cannot address the spurious association problem. In this work, we propose an end-toend reinforcement learning framework, which jointly performs counterfactual data generation and dual sentiment classification. Our approach has three characteristics: 1) the generator automatically generates massive and diverse antonymous sentences; 2) the discriminator contains a original-side sentiment predictor and an antonymous-side sentiment predictor, which jointly evaluate the quality of the generated sample and help the generator iteratively generate higher-quality antonymous samples; 3) the discriminator is directly used as the final sentiment classifier without the need to build an extra one. Extensive experiments show that our approach outperforms strong data augmentation baselines on several benchmark sentiment classification datasets. Further analysis confirms our approach’s advantages in generating more diverse training samples and solving the spurious association problem in sentiment classification

Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification

We describe the EdinSaar submission to the shared task of Multilingual Low-Resource Translation for North Germanic Languages at the Sixth Conference on Machine Translation (WMT2021). We submit multilingual translation models for translations to/from Icelandic (is), Norwegian-Bokmal (nb), and Swedish (sv). We employ various experimental approaches, including multilingual pre-training, back-translation, fine-tuning, and ensembling. In most translation directions, our models outperform other submitted systems.

EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT

We present the joint contribution of IST and Unbabel to the WMT 2021 Shared Task on Quality Estimation. Our team participated on two tasks: Direct Assessment and Post-Editing Effort, encompassing a total of 35 submissions. For all submissions, our efforts focused on training multilingual models on top of OpenKiwi predictor-estimator architecture, using pre-trained multilingual encoders combined with adapters. We further experiment with and uncertainty-related objectives and features as well as training on out-of-domain direct assessment data.

IST-Unbabel 2021 Submission for the Quality Estimation Shared Task

This paper describes Charles University sub-mission for Terminology translation shared task at WMT21. The objective of this task is to design a system which translates certain terms based on a provided terminology database, while preserving high overall translation quality. We competed in English-French language pair. Our approach is based on providing the desired translations alongside the input sentence and training the model to use these provided terms. We lemmatize the terms both during the training and inference, to allow the model to learn how to produce correct surface forms of the words, when they differ from the forms provided in the terminology database.

CUNI systems for WMT21: Multilingual Low-Resource Translation for Indo-European Languages Shared Task

This work introduces a simple regressive ensemble for evaluating machine translation quality based on a set of novel and established metrics. 
We evaluate the ensemble using a correlation to expert-based MQM scores of the WMT 2021 Metrics workshop.
In both monolingual and zero-shot cross-lingual settings, we show a significant performance improvement over single metrics.
In the cross-lingual settings, we also demonstrate that an ensemble approach is well-applicable to unseen languages.
Furthermore, we identify a strong reference-free baseline that consistently outperforms the commonly-used BLEU and METEOR measures and significantly improves our ensemble's performance.

Regressive Ensemble for Machine Translation Quality Evaluation

This paper presents the University of Edinburgh's constrained submissions of English-German and English-Hausa systems to the WMT 2021 shared task on news translation. We build En-De systems in three stages: corpus filtering, back-translation, and fine-tuning. For En-Ha we use an iterative back-translation approach on top of pre-trained En-De models and investigate vocabulary embedding mapping.

CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations

Downloads

Next from EMNLP 2021

Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study

Similar lecture

HittER: Hierarchical Transformers for Knowledge Graph Embeddings