Spain

Morphological inflection, like many sequence-to-sequence tasks, sees great performance from recurrent neural architectures when data is plentiful, but performance falls off sharply in lower-data settings. We investigate one aspect of 
neural seq2seq models that we hypothesize contributes to overfitting - teacher forcing. By creating different training and test conditions, exposure bias increases the likelihood that a system too closely models its training data. Experiments show that teacher-forced models struggle to recover when they enter unknown territory. However, a simple modification to the training algorithm to more closely mimic test conditions creates models that are better able to generalize to unseen 
environments.

COLING 2020

Noise Isn&#39;t Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models

morphology

low-resource

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Noise Isn't Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models

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.

Sentiment forecasting in dialog aims to predict the polarity of next utterance to come, and can help speakers revise their utterances in sentimental utterances generation. However, the polarity of next utterance is normally hard to predict, due to the lack of content of next utterance (yet to come). In this study, we propose a Neural Sentiment Forecasting (NSF) model to address inherent challenges. In particular, we employ a neural simulation model to simulate the next utterance based on the context (previous utterances encountered). Moreover, we employ a sequence influence model to learn both pair-wise and seq-wise influence. Empirical studies illustrate the importance of proposed sentiment forecasting task, and justify the effectiveness of our NSF model over several strong baselines. 


Sentiment Forecasting in Dialog

Through the progress made in a sentence-level neural machine translation (NMT), a context- aware NMT has been rapidly developed to exploit previous sentences as context. Recent work in the context-aware NMT incorporates source- or target-side contexts. In contrast to the source- side context, the target-side context causes a gap between training that utilizes a ground truth sentence and inference using a machine-translated sentence as context. This gap leads to transla- tion quality deteriorating because the translation model is trained with only the ground truth data that cannot be used in the inference. In this paper, we propose sampling both the ground truth and the machine-translated previous sentences of the target-side for the context-aware NMT. The proposed method can make the translation model robust against mistakes and biases made at the inference. Models using our proposed approach show improvements over models using the previous approaches in English ↔ Japanese and English ↔ German translation tasks.

Effective Use of Target-side Context for Neural Machine Translation

From the perspective of health psychology, human beings with long-term and sustained negativity are highly probable to be diagnosed with depression. Inspired by this, we argue that the global topic information derived from user-generated contents (e.g., texts and images) is crucial to boost the performance of the depression detection task, though this information has been neglected by
almost all previous studies on depression detection. To this end, we propose a new Multimodal Topic-enriched Auxiliary Learning (MTAL) approach, aiming at capturing the topic information inside different modalities (i.e., texts and images) for depression detection. Especially, in our approach, a modality-agnostic topic model is proposed to be capable of mining the topical clues from either the discrete textual signals or the continuous visual signals. On this basis, the topic modeling w.r.t. the two modalities are cast as two auxiliary tasks for improving the performance
of the primary task (i.e., depression detection). Finally, the detailed evaluation demonstrates the great advantage of our MTAL approach to depression detection over the state-of-the-art baselines. This justifies the importance of the multimodal topic information to depression detection and the effectiveness of our approach in capturing such information.

Multimodal Topic-Enriched Auxiliary Learning for 
Depression Detection


Recent question generation (QG) approaches often utilize the sequence-to-sequence framework (Seq2Seq) to optimize the log-likelihood of ground-truth questions using teacher forcing. However, this training objective is inconsistent with actual question quality, which is often reflected by certain global properties such as whether the question can be answered by the document. As such, we directly optimize for QG-specific objectives via reinforcement learning to improve question quality. We design three different rewards that target to improve the fluency, relevance, and answerability of generated questions. We conduct both automatic and human evaluations in addition to a thorough analysis to explore the effect of each QG-specific reward. We find that optimizing question-specific rewards generally leads to better performance in automatic evaluation metrics. However, only the rewards that correlate well with human judgment (e.g., relevance) lead to real improvement in question quality. Optimizing for the others, especially answerability, introduces incorrect bias to the model, resulting in poor question quality. Our code is publicly available at https://github.com/YuxiXie/RL-for-Question-Generation.

Exploring Question-Specific Rewards for Generating Deep Questions

Text generative models (TGMs) excel in producing text that matches the style of human language reasonably well. Such TGMs can be misused by adversaries, e.g., by automatically generating fake news and fake product reviews that can look authentic and fool humans. Detectors that can distinguish text generated by TGM from human written text play a vital role in mitigating such misuse of TGMs. Recently, there has been a flurry of works from both natural language processing (NLP) and machine learning (ML) communities to build accurate detectors for English. Despite the importance of this problem, there is currently no work that surveys this fast-growing literature and introduces newcomers to important research challenges. In this work, we fill this void by providing a critical survey and review of this literature to facilitate a comprehensive understanding of this problem. We conduct an in-depth error analysis of the state-of-the-art detector and discuss research directions to guide future work in this exciting area.

Automatic Detection of Machine Generated Text: A Critical Survey

Relation extraction (RE) has been extensively studied due to its importance in real-world applications such as knowledge base construction and question answering. Most of the existing works train the models on either distantly supervised data or human-annotated data. To take advantage of the high accuracy of human annotation and the cheap cost of distant supervision, we propose the dual supervision framework which effectively utilizes both types of data. However, simply combining the two types of data to train a RE model may decrease the prediction accuracy since distant supervision has labeling bias. We employ two separate prediction networks HA-Net and DS-Net to predict the labels by human annotation and distant supervision, respectively, to prevent the degradation of accuracy by the incorrect labeling of distant supervision. Furthermore, we propose an additional loss term called disagreement penalty to enable HA-Net to learn from distantly supervised labels. In addition, we exploit additional networks to adaptively assess the labeling bias by considering contextual information. Our performance study on sentence-level and document-level REs confirms the effectiveness of the dual supervision framework.

Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation

The paper introduces two Russian machine reading comprehension (MRC) datasets, calledMuSeRC and RuCoS, which require reasoning over multiple sentences and commonsense knowledge to infer the answer. The former follows the design of MultiRC, while the latter is a counterpart of the ReCoRD dataset. The datasets are included in RussianSuperGLUE, the Russian general language understanding benchmark. We provide a comparative analysis and demonstrate that the proposed tasks are relatively more complex as compared to the original ones for English.Besides, performance results of human solvers and BERT-based models show that MuSeRC and RuCoS represent a challenge for recent advanced neural models. We thus hope to facilitate research in the field of MRC for Russian and prompt the study of multi-hop reasoning in across-lingual scenario.

Read and Reason with MuSeRC and RuCoS: Datasets for Machine
Reading Comprehension for Russian

Traditional computational authorship attribution describes a classification task in a closed-set scenario. Given a finite set of candidate authors and corresponding labeled texts, the objective is to determine which of the authors has written another set of anonymous or disputed texts. 
In this work, we propose a probabilistic autoencoding framework to deal with this supervised classification task. More precisely, we are extending a variational autoencoder (VAE) with embedded Gaussian mixture model to a Student-t mixture model. 
Autoencoders have had tremendous success in learning latent representations. However, existing VAEs are currently still bound by limitations imposed by the assumed Gaussianity of the underlying probability distributions in the latent space. In this work, we are extending the Gaussian model for the VAE to a Student-t model, which allows for an independent control of the "heaviness" of the respective tails of the implied probability densities. 
Experiments over an Amazon review dataset indicate superior performance of the proposed method.

Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution

Language pairs with limited amounts of parallel data, also known as low-resource languages, remain a challenge for neural machine translation. While the Transformer model has achieved significant improvements for many language pairs and has become the de facto mainstream architecture, its capability under low-resource conditions has not been fully investigated yet. Our experiments on different subsets of the IWSLT14 training data show that the effectiveness of Transformer under low-resource conditions is highly dependent on the hyper-parameter settings. Our experiments show that using an optimized Transformer for low-resource conditions improves the translation quality up to 7.3 BLEU points compared to using the Transformer default settings.

Optimizing Transformer for Low-Resource Neural Machine Translation

In this work, we introduce a novel geometric algebra-based KG embedding framework, GeomE, which utilizes multivector representations and the geometric product to model entities and relations. Our framework subsumes several state-of-the-art KG embedding approaches and is advantageous with its ability of modeling various key relation patterns, including (anti-)symmetry, inversion and composition, rich expressiveness with higher degree of freedom as well as good generalization capacity. Experimental results on multiple benchmark knowledge graphs show that the proposed approach outperforms existing state-of-the-art models for link prediction.

Noise Isn't Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models

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