Spain

・We proposed data augmentation (DA) for zero anaphora resolution (ZAR)
・ We augmented labeled data by replacing tokens using language model (LM) 
・We improved the performance of ZAR and analyzed the phenomenon in DA

COLING 2020

An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution

predicate argument structure analysis

zero anaphora resolution

data augmentation

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

Large text corpora are increasingly important for a wide variety of Natural Language Processing (NLP) tasks, and automatic language identification (LangID) is a core technology needed to collect such datasets in a multilingual context. LangID is largely treated as solved in the literature, with models reported that achieve over 90% average F1 on as many as 1,366 languages. We train LangID models on up to 1,629 languages with comparable quality on held-out test sets, but find that human-judged LangID accuracy for web-crawl text corpora created using these models is only around 5% for many lower-resource languages, suggesting a need for more robust evaluation. Further analysis revealed a variety of error modes, arising from domain mismatch, class imbalance, language similarity, and insufficiently expressive models. We propose two classes of techniques to mitigate these errors: wordlist-based tunable-precision filters (for which we release curated lists in about 500 languages) and transformer-based semi-supervised LangID models, which increase median dataset precision from 5.5% to 71.2%. These techniques enable us to create an initial data set covering 100K or more relatively clean sentences in each of 500+ languages, paving the way towards a 1,000-language web text corpus.

Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus

We propose a novel dataset TG-ReDial (Recommendation through Topic-Guided Dialog) for conversational recommendation system (CRS), in which we emphasize natural topic transitions from non-recommendation scenarios to the desired recommendation scenario.


Towards Topic-Guided Conversational Recommender System

We study review generation from attribute identifiers such as user ID, product ID, and rating.
We utilize existing reviews of given user/product as references for attribute-consistent review generation.
Two training strategies are proposed to train reference retriever, enabling efficient and effective retrieval.

Retrieval-Augmented Controllable Review Generation

The COVID-19 pandemic spotlighted the importance of moving quickly with biomedical research. However, the task of finding relevant articles to answer pressing questions has become significant. In this work, we propose a textual data mining tool that supports a literature search to accelerate the work of researchers in the biomedical domain. We achieve this by building BioMedBERT, a neural-based deep contextual understanding model for Question-Answering, and Information Retrieval tasks.

BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR

Transformer becomes the state-of-the-art translation model, while it is not well studied how each intermediate component contributes to the model performance, which poses significant challenges for designing optimal architectures. In this work, we bridge this gap by evaluating the impact of individual component (sub-layer) in trained Transformer models from different perspectives. Experimental results across language pairs, training strategies, and model capacities show that certain components are consistently more important than the others. We also report a number of interesting findings that might help humans better analyze, understand and improve Transformer models. Based on these observations, we further propose a new training strategy that can improves translation performance by distinguishing the unimportant components in training.

Rethinking the Value of Transformer Components

Visualization and topic modeling are widely used approaches for text analysis. Traditional visualization methods find low-dimensional representations of documents in the visualization space (typically 2D or 3D) that can be displayed using a scatterplot. In contrast, topic modeling aims to discover topics from text, but for visualization, one needs to perform a post-hoc embedding using dimensionality reduction methods. Recent approaches propose using a generative model to jointly find topics and visualization, allowing the semantics to be infused in the visualization
space for a meaningful interpretation. A major challenge that prevents these methods from being used practically is the scalability of their inference algorithms. We present, to the best of our knowledge, the first fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization. Since our method is black box, it can handle model changes efficiently with little mathematical rederivation effort. We demonstrate the efficiency and effectiveness of our method on real-world large datasets and compare it with existing baselines.

Auto-Encoding Variational Bayes for Inferring Topics and Visualization

Text-to-SQL systems offers natural language interfaces to databases, which can automatically generates SQL queries given natural language questions. On the WikiSQL benchmark, state-ofthe-art text-to-SQL systems typically take a slot-filling approach by building several specialized models for each type of slot. Despite being effective, such modularized systems are complex and also fall short in jointly learning for different slots. To solve these problems, this paper proposes a novel approach that formulates the task as a question answering problem, where different slots are predicted by a unified machine reading comprehension (MRC) model. For this purpose, we use a BERT-based MRC model, which can also benefit from intermediate training on other MRC datasets. The proposed method can achieve competitive results on WikiSQL, suggesting it being a promising direction for text-to-SQL.

SQL Generation via Machine Reading Comprehension

This paper proposed a supervised visual attention mechanism for multimodal neural machine translation (MNMT), trained with constraints based on manual alignments between words in a sentence and their corresponding regions of an image. 
The proposed visual attention mechanism captures the relationship between a word and an image region more precisely than a conventional visual attention mechanism trained through MNMT in an unsupervised manner. 
Our experiments on English-German and German-English translation tasks using the Multi30k dataset and on English-Japanese and Japanese-English translation tasks using the Flickr30k Entities JP dataset show that a Transformer-based MNMT model can be improved by incorporating our proposed supervised visual attention mechanism and that further improvements can be achieved by combining it with a supervised cross-lingual attention mechanism (up to +1.61 BLEU, +1.7 METEOR).

Supervised Visual Attention for Multimodal Neural Machine Translation

We present a large-scale corpus of e-mail conversations with domain-agnostic and two-level dialogue act (DA) annotations towards the goal of a better understanding of asynchronous conversations. To assess the difficulty of DA recognition on our corpus, we evaluate several models, including a pre-trained contextual representation model, as our baselines. The experimental results show that BERT outperforms other neural network models, including previous state-of-the-art models, but falls short of a human performance. An evaluation of a model trained on our corpus against other domains of asynchronous conversation reveals the domain independence of our DA annotations.

A Large-Scale Corpus of E-mail Conversations with Standard and Two-Level Dialogue Act Annotations

Multi-turn response selection has been extensively studied and applied to many real-world applications in recent years. However, current methods typically model the interactions between multi-turn utterances and candidate responses with iterative approaches, which is not practical as the turns of conversations vary. Besides, some latent features, such as user intent and conversation topic, are under-discovered in existing works. In this work, we propose Intra-/Inter-Interaction Network (I3) with latent interaction modeling to comprehensively model multi-level interactions between the utterance context and the response. In specific, we first encode the intra- and inter-utterance interaction with the given response from both individual utterance and the overall utterance context. Then we develop a latent multi-view subspace clustering module to model the latent interaction between the utterance and response. Experimental results show that the proposed method substantially and consistently outperforms existing state-of-the-art methods on three multi-turn response selection benchmark datasets.

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