Spain

End-to-end neural data-to-text (D2T) generation has recently emerged as an alternative to pipeline-based architectures. However, it has faced challenges in generalizing to new domains and generating semantically consistent text. In this work, we present DataTuner, a neural, end-to-end data-to-text generation system that makes minimal assumptions about the data representation and the target domain. We take a two-stage generation-reranking approach, combining a fine-tuned language model with a semantic fidelity classifier. Each of our components is learnt end-to-end without the need for dataset-specific heuristics, entity delexicalization, or post-processing. We show that DataTuner achieves state of the art results on the automated metrics across four major D2T datasets (LDC2017T10, WebNLG, ViGGO, and Cleaned E2E), with a fluency assessed by human annotators nearing or exceeding the human-written reference texts. We further demonstrate that the model-based semantic fidelity scorer in DataTuner is a better assessment tool compared to traditional, heuristic-based measures. Our generated text has a significantly better semantic fidelity than the state of the art across all four datasets.

COLING 2020

Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity

neural nlg

webnlg

data-to-text

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

technical paper

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Multimodal sentiment analysis aims to predict sentiment of language text with the help of other modalities, such as vision and acoustic features. Previous studies focused on learning the joint representation of multiple modalities, ignoring some useful knowledge contained in language modal.
In this paper, we try to incorporate sentimental words knowledge into the fusion network to guide the learning of joint representation of multimodal features. Our method consists of two components: shallow fusion part and aggregation part. For the shallow fusion part, we use crossmodal coattention mechanism to obtain bidirectional context information of each two modals to get the fused shallow representations. For the aggregation part, we design a multitask of sentimental words classification to help and guide the deep fusion of the three modalities and obtain the final sentimental words aware fusion representation. We carry out several experiments on CMU-MOSI, CMU-MOSEI and YouTube datasets. The experimental results show that introducing sentimental words prediction as a multitask can really improve the fusion representation of multiple modalities.

SWAFN: Sentimental Words Aware Fusion Network for Multimodal Sentiment Analysis

We release an urgency dataset that consists of English tweets relating to natural crises. The set is annotated along with annotations of their corresponding urgency status. Additionally, we release evaluation datasets for two low-resource languages, i.e. Sinhala and Odia, and demonstrate an effective zero-shot transfer from English to these two languages by training cross-lingual classifiers. We adopt cross-lingual embeddings constructed using different methods to extract features of the tweets, including a few state-of-the-art contextual embeddings such as BERT, RoBERTa and XLM-R. We train a variety of classifier architectures, supervised and semi supervised, on the extracted features. We also further experiment with ensembling the various classifiers. With very limited amounts of labeled data in English and zero data in the low resource languages, we show a successful framework of training monolingual and cross-lingual classifiers using deep learning methods which are known to be data hungry. Specifically, we show that the recent deep contextual embeddings are also helpful when dealing with very small-scale datasets. Classifiers that incorporate RoBERTa yield the best performance for the English urgency detection task, with 25% F1 score absolute improvement over the baselines. For the zero-shot transfer to low resource languages, classifiers that use LASER features perform the best for Sinhala transfer while XLM-R features benefit the Odia transfer the most.

Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages

In this study, we explore cross-lingual transfer learning in grammatical error correction (GEC) tasks. Many languages lack the resources required to train GEC models. Cross-lingual transfer learning from high-resource languages (the source models) is effective for training models of low-resource languages (the target models) for various tasks. However, in GEC tasks, the possibility of transferring grammatical knowledge (e.g., grammatical functions) across languages is not evident.
Therefore, we investigate cross-lingual transfer learning methods for GEC. Our results demonstrate that transfer learning from other languages can improve the accuracy of GEC. We also demonstrate that proximity to source languages has a significant impact on the accuracy of correcting certain types of errors.

Cross-lingual Transfer Learning for Grammatical Error Correction

Presents our study of cloze-style reading comprehension by imitating human reading comprehension. The outline includes: Motivations, Model part 1 - Fusion Representation, Model part 2 - Multi-choice Relational Reasoning, Experiments, Conclusion


Multi-choice Relational Reasoning for Machine Reading Comprehension

Lexical semantics theories differ in advocating that the meaning of words is represented as an inference graph, a feature mapping or a vector space, thus raising the question: is it the case that one of these approaches is superior to the others in representing lexical semantics appropriately?

Or in its non antagonistic counterpart: could there be a unified account of lexical semantics where these approaches seamlessly emerge as (partial) renderings of (different) aspects of a core semantic knowledge base?

In this paper, we contribute to these research questions with a number of 
experiments that systematically probe different lexical semantics theories for their levels of cognitive plausibility and of technological usefulness.

The empirical findings obtained from these experiments advance our insight on lexical semantics as the feature-based approach emerges as superior to the other ones, and arguably also move us closer to finding answers to the research questions above.

Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness

We conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attention (Elbayad et al. 2018) and attention-based Transformer (Vaswani et al. 2017) are considered. We investigate, for both architectures, the impact of online decoding constraints on the translation quality through a carefully designed human evaluation on English-German and German-English language pairs, the latter being particularly sensitive to latency constraints. The evaluation results allow us to identify the strengths and shortcomings of each model when we shift to the online setup.

Online Versus Offline NMT Quality: An In-depth Analysis on English–German and German–English

Knowledge graph embedding maps entities and relations into low-dimensional vector space. However, it is still challenging for many existing methods to model diverse relational patterns, especially symmetric and antisymmetric relations. To address this issue, we propose a novel model, AprilE, which employs triple-level self-attention and pseudo residual connection to model relational patterns. The triple-level self-attention treats head entity, relation, and tail entity as a sequence and captures the dependency within a triple. At the same time the pseudo residual
connection retains primitive semantic features. Furthermore, to deal with symmetric and antisymmetric relations, two schemas of score function are designed via a position-adaptive mechanism. Experimental results on public datasets demonstrate that our model can produce expressive knowledge embedding and significantly outperforms most of the state-of-the-art works.

AprilE: Attention with Pseudo Residual Connection for Knowledge Graph Embedding

Prior work investigating the geometry of pre-trained word embeddings have shown that word embeddings to be distributed in a narrow cone and by centering and projecting using principal component vectors one can increase the accuracy of a given set of pre-trained word embeddings. However, theoretically, this post-processing step is equivalent to applying a linear autoencoder to minimise the squared l2 reconstruction error. This result contradicts prior work (Mu and Viswanath, 2018) that proposed to remove the top principal components from pre-trained embeddings. We experimentally verify our theoretical claims and show that retaining the top principal components is indeed useful for improving pre-trained word embeddings, without requiring access to additional linguistic resources or labelled data.

Autoencoding Improves Pre-trained Word Embeddings

We design the positional dependency-based word embedding (PoD) which considers both dependency context and positional context for aspect term extraction.

PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction

We attempt to bridge the gap between human comments and generated comments and the diversity of generated comments is our goal. We propose a multi-perspective mechanism to enhance automatic music comment. To optimize the multi-perspective components better, we designed a novel comment generation model based on multiple-perspective extraction training. We improve the differentia degree of comments and generate One Comment from One Perspective.

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