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Conversational Machine Reading (CMR) aims at answering questions in complicated interactive scenarios. Machine needs to answer questions through interactions with users based on given rule document, user scenario and dialogue history, and even initiatively asks questions for clarification if necessary. Namely, the answer to the task needs a machine in the response of either &quot;Yes, No, Irrelevant&quot; or to raise a follow-up question for further clarification. To effectively capture multiple objects in such a challenging task, graph modeling is supposed to be adopted, though it is surprising that this does not happen until this work proposes a dialogue graph modeling framework by incorporating two complementary graph models, i.e., explicit discourse graph and implicit discourse graph, which respectively capture explicit and implicit interactions hidden in the rule documents. The proposed model is evaluated on the ShARC benchmark and achieves new state-of-the-art by first exceeding the milestone accuracy score of 80%.

ACL-IJCNLP 2021

Dialogue Graph Modeling for Conversational Machine Reading

language representation

dialogue modeling

graph convolutional network

conversational question answering

natural language processing

**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). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

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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).

Conversational Machine Reading (CMR) aims at answering questions in complicated interactive scenarios. Machine needs to answer questions through interactions with users based on given rule document, user scenario and dialogue history, and even initiatively asks questions for clarification if necessary. Namely, the answer to the task needs a machine in the response of either "Yes, No, Irrelevant" or to raise a follow-up question for further clarification. To effectively capture multiple objects in such a challenging task, graph modeling is supposed to be adopted, though it is surprising that this does not happen until this work proposes a dialogue graph modeling framework by incorporating two complementary graph models, i.e., explicit discourse graph and implicit discourse graph, which respectively capture explicit and implicit interactions hidden in the rule documents. The proposed model is evaluated on the ShARC benchmark and achieves new state-of-the-art by first exceeding the milestone accuracy score of 80%.

technical paper

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

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.

Morphology Matters: A Multilingual Language Modeling Analysis

Arabic diacritization is a fundamental task for Arabic language processing. Previous studies have demonstrated that automatically generated knowledge can be helpful to this task. However, these studies regard the auto-generated knowledge instances as gold references, which limits their effectiveness since such knowledge is not always accurate and inferior instances can lead to incorrect predictions. In this paper, we propose to use regularized decoding and adversarial training to appropriately learn from such noisy knowledge for diacritization. Experimental results on two benchmark datasets show that, even with quite flawed auto-generated knowledge, our model can still learn adequate diacritics and outperform all previous studies, on both datasets.

Improving Arabic Diacritization with Regularized Decoding and Adversarial Training

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.

Dialogue Graph Modeling for Conversational Machine Reading

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