Thailand

Prior studies in multilingual language modeling (e.g., Cotterell et al., 2018; Mielke et al., 2019) disagree on whether or not inflectional morphology makes languages harder to model. We attempt to resolve the disagreement and extend those studies. We compile a larger corpus of 145 Bible translations in 92 languages and a larger number of typological features. We fill in missing typological data for several languages and consider corpus-based measures of morphological complexity in addition to expert-produced typological features. We find that several morphological measures are significantly associated with higher surprisal when LSTM models are trained with BPE-segmented data. We also investigate linguistically motivated subword segmentation strategies like Morfessor and Finite-State Transducers (FSTs) and find that these segmentation strategies yield better performance and reduce the impact of a language’s morphology on language modeling.

IJCNLP-AACL 2021

Morphology Matters: A Multilingual Language Modeling Analysis

morphological complexity

linguistic typology

language modeling

multilingual

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

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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).

technical paper

Distantly supervision automatically generates plenty of training samples for relation extraction. 
However, it also incurs two major problems: noisy labels and imbalanced training data.
Previous works focus more on reducing wrongly labeled relations (false positives) while few explore the missing relations that are caused by incompleteness of knowledge base (false negatives).
Furthermore, the quantity of negative labels overwhelmingly surpasses the positive ones in previous problem formulations.
In this paper, we first provide a thorough analysis of the above challenges caused by negative data.
Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem. 
Thirdly, we propose a pipeline approach, dubbed \textsc{ReRe}, that first performs sentence classification with relational labels and then extracts the subjects/objects.
Experimental results show that the proposed method consistently outperforms existing approaches and remains excellent performance even learned with a large quantity of false positive samples.
Source code is available online at https://github.com/redreamality/RERE-relation-extraction.

Revisiting the Negative Data of Distantly Supervised Relation Extraction

This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the problem of dangling entities. As the first attempt to this problem, we construct a new dataset and design a multi-task learning framework for both entity alignment and dangling entity detection. The framework can opt to abstain from predicting alignment for the detected dangling entities. We propose three techniques for dangling entity detection that are based on the distribution of nearest-neighbor distances, i.e., nearest neighbor classification, marginal ranking and background ranking. After detecting and removing dangling entities, an incorporated entity alignment model in our framework can provide more robust alignment for remaining entities. Comprehensive experiments and analyses demonstrate the effectiveness of our framework. We further discover that the dangling entity detection module can, in turn, improve alignment learning and the final performance. The contributed resource is publicly available to foster further research.

Knowing the No-match: Entity Alignment with Dangling Cases

We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language. Our proposed method leverages rhetorical discourse features and temporal arguments from semantic role labels, in addition to traditional local syntactic features, trained through a Gated Relational-GCN. Extensive experiments show that the proposed model outperforms previous methods by 5-18\% on the TDDiscourse, TimeBank-Dense, and MATRES datasets due to our discourse-level modeling.

TIMERS: Document-level Temporal Relation Extraction

How does the input segmentation of pretrained language models (PLMs) 
affect their interpretations of complex words?
We present the first study investigating this question,
taking BERT as the example PLM and focusing on its semantic representations
of English derivatives.
We show that PLMs can be interpreted as serial dual-route models, i.e.,
the meanings of complex words are either stored or else need to 
be computed from the subwords,
which implies that maximally meaningful input tokens should
allow for the best generalization on new words. This hypothesis
is confirmed by a series of semantic probing 
tasks on which DelBERT (Derivation leveraging BERT), a model with 
derivational input segmentation, substantially outperforms
BERT with WordPiece segmentation. Our results suggest
that the generalization capabilities of PLMs
could be further improved if a morphologically-informed vocabulary 
of input tokens were used.

Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words

We present a novel model for the problem of ranking a collection of documents according to their semantic similarity to a source (query) document. While the problem of document-to-document similarity ranking has been studied, most modern methods are limited to relatively short documents or rely on the existence of "ground-truth'' similarity labels. Yet, in most common real-world cases, similarity ranking is an unsupervised problem as similarity labels are unavailable. Moreover, an ideal model should not be restricted by documents' length. Hence, we introduce SDR, a self-supervised method for document similarity that can be applied to documents of arbitrary length. 
Importantly, SDR can be effectively applied to extremely long documents, exceeding the $4,096$ maximal token limit of Longformer.
Extensive evaluations on large document datasets show that SDR significantly outperforms its alternatives across all metrics. To accelerate future research on unlabeled long document similarity ranking, and as an additional contribution to the community, we herein publish two human-annotated test sets of long documents similarity evaluation. The SDR code and datasets are publicly available.

Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference

Recent years have seen many breakthroughs in natural language processing (NLP), transitioning it from a mostly theoretical field to one with vast real-world applications. Noting precursor applications in other machine learning and AI techniques with pervasive societal impact, we anticipate the rising importance of developing NLP technologies for social good. Inspired by theories in moral philosophy and global priority research, we aim to promote a future guideline for social good in the context of NLP. We lay the foundations via \textit{moral philosophy}'s definition of social good, and propose a framework to provide a categorization of NLP tasks on the basis of real-world impact, along with metrics to calculate the expected social impact of NLP technology. Based on these fundamental frameworks, we adopt the methodology of global priority research to identify priority causes for NLP research, and use thought experiments to illustrate research priority under hypothetical situations. Finally, we use our theoretical framework to quantify ACL 2020 papers on the basis of societal impact, and provide a checklist for ensuring the creation of NLP technologies that advance social good. Based on these tools, we envision new usage of NLP to effectively improve social good and call attention to important NLP problems with the potential for strong social impact.

How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact

In this work, we propose a new task called Image-guided Story Ending Generation (IgSEG). Given a multi-sentence story plot and an ending-related image, IgSEG aims to generate a story ending that conforms to the contextual logic and the relevant visual concepts. In contrast to the story ending generation task, which generates open-ended endings, the major challenges of IgSEG are to comprehend the given context and image sufficiently, and mine the appropriate semantics from the image to make the generated story ending informative, reasonable, and coherent. To address the challenges, we propose a Multi-layer Graph convolution and Cascade-LSTM (MGCL) based model 
which mainly comprises of two collaborative modules: i) a multi-layer graph convolutional network to learn the dependency relations of sentences and the logical clue of the context; ii) a multiple context-image attention module to generate the story endings by gradually incorporating textual and visual semantic concepts. Our MGCL is thus capable of building logically consistent and semantically rich story endings. To evaluate the proposed model, we modify the existing VIST dataset to obtain the VIST-Ending dataset. Empirically, our MGCL outperforms all the strong baselines on both automatic and human evaluation.

IgSEG: Image-guided Story Ending Generation

Query focused summarization (QFS) models aim to generate summaries from source documents that can answer the given query. Most previous work on QFS only considers the query relevance criterion when producing the summary. However, studying the effect of answer relevance in the summary generating process is also important. In this paper, we propose QFS-BART, a model that incorporates the explicit answer relevance of the source documents given the query via a question answering model, to generate coherent and answer-related summaries. Furthermore, our model can take advantage of large pre-trained models which improve the summarization performance significantly. Empirical results on the Debatepedia dataset show that the proposed model achieves the new state-of-the-art performance.

Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance

We propose a Bi-Directional Manifold Alignment (BDMA) that learns a non-linear mapping between two manifolds by explicitly training it to be bijective. We demonstrate BDMA by training a model for a pair of languages rather than individual, directed source and target combinations, reducing the number of models by 50%. We show that models trained with BDMA in the "forward'' (source to target) direction can successfully map words in the "reverse'' (target to source) direction, yielding equivalent (or better) performance to standard unidirectional translation models where the source and target language is flipped. We also show how BDMA reduces the overall size of the model.

Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment

In this paper, we investigate reasoning over natural language based on modern neural networks.
Prior efforts such as PRover train the question answering module and proof generation module through a multi-task classification framework, which does not consider explicit inter-dependency among nodes and edges in the proof graph.
In this paper, we propose PRobr,
a novel approach for answer prediction and proof generation.
PRobr defines a joint probabilistic distribution over all possible proofs and answers via an induced graphical model. 
We then optimize the model using variational approximation on top of neural textual representation. 
Experiments on several datasets verify the effectiveness of PRobr under multiple diverse settings (fully supervised, few-shot and zero-shot evaluation),
e.g., achieving 10\%-30\% improvement on QA accuracy in few/zero-shot evaluation.

Morphology Matters: A Multilingual Language Modeling Analysis

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