Spain

We propose a distance-based model aiming at temporal knowledge graph embedding (TKGE). The temporal evolution of an entity embedding is defined as a rotation from the initial time to the current time in the complex vector space. Each relation is represented as a pair of dual complex embeddings to handle the beginning and the end of the relation, respectively. We use different time division methods and time granularities for different datasets. Experimental Results show that our model achieves the state-of-the-art performances on four TKG datasets.


COLING 2020

TeRo: A Time-aware Knowledge Graph Embedding Model via Temporal Rotation

embedding model

temporal knowledge graph

link prediction

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.

poster

Lexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone of various NLP applications, including word sense induction and disambiguation, lexical relation extraction, data augmentation, etc. In this paper, we present a large-scale comparative study of lexical substitution methods employing both rather old and most recent language and masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, RoBERTa, XLNet. We show that already competitive results achieved by SOTA LMs/MLMs can be further substantially improved if information about the target word is injected properly. Several existing and new target word injection methods are compared for each LM/MLM using both intrinsic evaluation on lexical substitution datasets and extrinsic evaluation on word sense induction (WSI) datasets. On two WSI datasets we obtain new SOTA results. Besides, we analyze the types of semantic relations between target words and their substitutes generated by different models or given by annotators.

Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution

Common uncertainty estimation approaches remain as black-boxes without explaining which features have led to the uncertainty of a prediction. We introduce an approach to decompose and visualize the uncertainty of text classifiers at the level of words. We aim to enable a deeper inspection and reasoning about unreliable model behaviours by providing detailed explanations of uncertainties.

Word-Level Uncertainty Estimation for Black-Box Text Classifiers using RNNs

This poster introduces the SORTS Subject-Object Resolution Test Suite of German minimal sentence pairs for model introspection. The full test suite consists of 18,502 transitive clauses with manual annotations of 8 word order patterns, 5 morphological and syntactic and 11 semantic property classes. The test suite has been constructed such that sentences are minimal pairs with respect to a property class. Each property has been selected with a particular focus on its effect on subject-object resolution, the second-most error-prone task within syntactic parsing of German after prepositional phrase attachment. The size and detail of annotations make the test suite a valuable resource for natural language processing applications with syntactic and semantic tasks. We use dependency parsing to demonstrate how the test suite allows insights into the process of subject-object resolution. Based on the test suite annotations, word order and case syncretism can be identified as most important factors that affect subject-object resolution.

When Beards Start Shaving Men: A Subject-object Resolution Test Suite for Morpho-syntactic and Semantic Model Introspection

Many previous approaches use translation data by translating from a rich-source language, such as English, to low-source languages as auxiliary supervision. However, how to effectively leverage translation data and reduce the impact of noise introduced by translation remains onerous. In this paper, we tackle these challenges by an augmentation approach named Language Branch Machine Reading Comprehension (LBMRC). A language branch is a group of passages in one single language paired with questions in all target languages (see Figure 1). We train multiple MRC models proficient in individual language based on LBMRC. Then, we devise a multilingual multi-teacher distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages. Our method can be more robust to the data noises, therefore, improve the model‘s cross-lingual ability.

Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation

This paper introduces a state-of-the-art pseudo-labeling technique for few-shot intent detection. Our folding/unfolding hierarchical clustering algorithm, which assigns weighted pseudo-labels to unlabeled user utterances, is a two-step method yielding significant improvement over existing solutions. This performance is achieved on multiple intent detection datasets, as well as the more general text classification task. Our approach nicely complements existing solutions, thereby providing an even stronger state-of-the-art ensemble method.

Few-Shot Pseudo-Labeling for Intent Detection

Motivated by applications such as question answering, fact checking, and data integration, there is significant interest in constructing knowledge graphs by extracting information from unstructured information sources, particularly text documents. Knowledge graphs have emerged as a standard for structured knowledge representation, whereby entities and their inter-relations are represented and conveniently stored as (subject, predicate, object) triples in a graph that can be used to power various downstream applications. The proliferation of financial news sources reporting on companies, markets, currencies, and stocks presents an opportunity for extracting valuable knowledge about this crucial domain. In this paper, we focus on constructing a knowledge graph automatically by information extraction from a large corpus of financial news articles. For that purpose, we develop a high precision knowledge extraction pipeline tailored for the financial domain. This pipeline combines multiple information extraction techniques with a financial dictionary that we built, all working together to produce over 342,000 compact extractions from over 288,000 financial news articles, with a precision of 78% at the top-100 extractions. The extracted triples are stored in a knowledge graph making them readily available for use in downstream applications.

A High Precision Pipeline for Financial Knowledge Graph Construction

Recent research on the multi-head attention mechanism, especially that in pre-trained models such as BERT, has shown us heuristics and clues in analyzing various aspects of the mechanism. As most of the research focus on probing tasks or hidden states, previous works have found some primitive patterns of attention head behavior by heuristic analytical methods, but a more systematic analysis specific on the attention patterns still remains primitive. In this work, we clearly cluster the attention heatmaps into significantly different patterns through unsupervised clustering on top of a set of proposed features, which corroborates with previous observations. We further study their corresponding functions through analytical study. In addition, our proposed features can be used to explain and calibrate different attention heads in Transformer models.


How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT's Attention

We propose neural models that can normalize text by considering the similarities of word strings and sounds. We experimentally compared a model that considers the similarities of both word strings and sounds, a model that considers only the similarity of word strings or of sounds, and a model without the similarities as a baseline. Results showed that leveraging the word string similarity succeeded in dealing with misspellings and abbreviations, and taking into account the sound similarity succeeded in dealing with phonetic substitutions and emphasized characters. So that the proposed models achieved higher F1 scores than the baseline.

Neural text normalization leveraging similarities of strings and sounds

Children have less linguistic skills than adults, which makes it more difficult for them to understand some texts, for instance when browsing the Internet. In this context, we present a novel method which predicts the minimal age from which a text can be understood. This method analyses each sentence of a text using a recurrent neural network, and then aggregates this information to provide the text-level prediction. Different approaches are proposed and compared to baseline models, at sentence and text levels.
Experiments are carried out on a corpus of 1,500 texts and 160K sentences. Our best model, based on LSTMs, outperforms state-of-the-art results and achieves mean absolute errors of 1.86 and 2.28, at sentence and text levels, respectively.

Mama/Papa, Is this Text for Me?

Traditional word vectors, such as word2vec and glove, have a well-known inclination to conflate the semantic similarity with other semantic relations. As a result, the synonym and antonym relationships between words might not be well reflected in word embeddings. Namely, if two words are antonyms (synonyms), their corresponding word vectors could be very close (far) geometrically. Lots of downstream applications would be influenced by the property. For instance, if “good" and “bad" are close to each other, it would be hard to distinguish the sentiment polarity of a sentence. In this paper, we propose a new method called HRSWE. It has a competitive specialization performance compared with a strong baseline while runs dramatically faster and is more interpretable.

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Next from COLING 2020

Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution

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