Spain

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


COLING 2020

Multi-choice Relational Reasoning for Machine Reading Comprehension

cloze tasks.

multi-choice relational reasoning

fusion representation

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
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, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 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 and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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.

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.

One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment

We propose a family of metrics to assess language generation derived from population estimation methods widely used in ecology. More specifically, we use mark-recapture and maximum-likelihood methods that have been applied over the past several decades to estimate the size of closed populations in the wild. We propose three novel metrics: ME_Petersen and ME_CAPTURE, which retrieve a single-valued assessment, and ME_Schnabel which returns a double-valued metric to assess the evaluation set in terms of quality and diversity, separately. In synthetic experiments, our family of methods is sensitive to drops in quality and diversity. Moreover, our methods show a higher correlation to human evaluation than existing metrics on several challenging tasks, namely unconditional language generation, machine translation, and text summarization.

Mark-Evaluate: Assessing Language Generation using Population Estimation Methods

In Text-to-SQL semantic parsing, selecting the correct entities (tables and columns) for the generated SQL query is both crucial and challenging; the parser is required to connect the natural language (NL) question and the SQL query to the structured knowledge in the database. We formulate two linking processes to address this challenge: schema linking which links explicit NL mentions to the database and structural linking which links the entities in the output SQL with their structural relationships in the database schema. Intuitively, the effectiveness of these two linking processes changes based on the entity being generated, thus we propose to dynamically choose between them using a gating mechanism. Integrating the proposed method with two graph neural network-based semantic parsers together with BERT representations demonstrates substantial gains in parsing accuracy on the challenging Spider dataset. Analyses show that our proposed method helps to enhance the structure of the model output when generating complicated SQL queries and offers more explainable predictions.

A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing

We propose a novel idea to explicitly utilise historical reviews of a given user and product in order to learn a better representations for user and product as well as to model user and product preferences more accurately for improving sentiment analysis. Experimental results demonstrate the effectiveness of our proposed model on three benchmark datasets: IMDB, Yelp-2013 and Yelp-2014.

Improving Document-Level Sentiment Analysis with User and Product Context

Text-to-image multimodal tasks, generating/retrieving an image from a given text description, are extremely challenging tasks since raw text descriptions cover quite limited information in order to fully describe visually realistic images. We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input. First, we use the text description as initial input and conduct dependency parsing to extract the syntactic structure and analyse the semantic aspect, including object quantities, to extract the scene graph. Then, we train the extracted objects, attributes, and relations in the scene graph and the corresponding geometric relation information using Graph Convolutional Networks, and it generates text representation which integrates textual and visual semantic information. The text representation is aggregated with word-level and sentence level embedding to generate both visual contextual word and sentence representation. For the evaluation, we attached VICTR to the state-of-the-art models in text-to-image generation.VICTR is easily added to existing models and improves across both quantitative and qualitative aspects.

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