Spain

As education switches from the traditional classroom environment to online education and assessment, teachers could not follow students’ learning behavior closely which leads to inaccurate assessment of students’ knowledge. A well-defined method to precisely measure difficulty of questions is critical as it can guide teachers in assessing students’ performance and help provide customized attention to students. In this paper, we explore the concept of question difficulty and share our new Chinese Driving Test Question Difficulty Comprehension (DT-QDC) dataset. Different from the existing question datasets, we mainly design for question evaluation and comprehension in online testing, so each question has enriched attributes and difficulty label.Additional attributes such as keywords, chapter, and question type would allow models to understand questions more precisely. We proposed the Ordinal Regression Multi-Source BERT (ORMS-BERT) model to assess the difficulty of the question, which outperforms different baselines by 6.77% on F1-score and 15.92% on MAE, 28.26% on MSE on the DT-QDC dataset, laying the foundation for the question difficulty comprehension task.

COLING 2020

DT-QDC: A Dataset for Question Comprehension in Online Test

online test

question comprehension

dataset

**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.


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ACL-IJCNLP 2021

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

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.

This paper proposes a general purpose relation extractor that uses Wikidata descriptions to represent the relation's surface form.
The results are tested on the FewRel 1.0 dataset, which provides an excellent framework for training and evaluating the proposed zero-shot learning system in English. 
This relation extractor architecture exploits the implicit knowledge of a language model through a question-answering approach.

Exploring the Zero Shot Limit of FewRel

This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource. We show that our method provides a substantially more accurate characterization of atypical animacy, especially when applied to highly complex forms of language use.

Living Machines: A study of atypical animacy

Automatically describing videos in natural language is an ambitious problem, which could bridge our understanding of vision and language. We propose a hierarchical approach, by first generating video descriptions as sequences of simple sentences, followed at the next level by a more complex and fluent description in natural language. While the simple sentences describe simple actions in the form of
(subject, verb, object), the second-level paragraph descriptions, indirectly using information from the first-level description, presents the visual content in a more compact, coherent and semantically rich manner. 
To this end, we introduce the first video dataset in the literature that is annotated with captions at two levels of linguistic complexity. We perform extensive tests that demonstrate that our hierarchical linguistic representation, from simple to complex language, allows us to train a two-stage network that is able to generate significantly more complex paragraphs than current one-stage approaches.

A hierarchical approach to vision-based language generation: from simple sentences to complex natural language

The interpretation of the lexical aspect of verbs in English plays a crucial role for recognizing textual entailment and learning discourse-level inferences.
We show that two elementary dimensions of aspectual class, states vs. events, and telic vs. atelic events, can be modelled effectively with distributional semantics. We find that a verb's local context is most indicative of its aspectual class, and demonstrate that closed class words tend to be stronger discriminating contexts than content words. Our approach outperforms previous work on three datasets. Lastly, we contribute a dataset of human--human conversations annotated with lexical aspect and present experiments that show the correlation of telicity with genre and discourse goals.

Aspectuality Across Genre: A Distributional Semantics Approach

We unveil the language encoded in sentence embeddings by conditionally generating from them. We perceive of this as a new unsupervised probing task and show that it correlates well with downstream task performance. We also illustrate how the language generated from different encoders differs. We apply our approach to generate sentence analogies from sentence embeddings.

Vec2Sent: Probing Sentence Embeddings With Natural Language Generation

In many domains, dialogue systems need to work collaboratively with users to successfully reconstruct the meaning the user had in mind. In this paper, we show how cognitive models of users' communicative strategies can be leveraged in a reinforcement learning approach to dialogue planning to enable interactive systems to give targeted, effective feedback about the system's understanding. We describe a prototype system that collaborates on reference tasks that distinguish arbitrarily varying color patches from similar distractors, and use experiments with crowd workers and analyses of our learned policies to document that our approach leads to context-sensitive clarification strategies that focus on key missing information, elicit correct answers that the system understands, and contribute to increasing dialogue success.

Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue

While lexico-semantic elements no doubt capture a large amount of linguistic information, it has been argued that they do not capture all information contained in text. This assumption is central to constructionist approaches to language which argue that language consists of constructions, learned pairings of a form and a function or meaning that are either frequent or have a meaning that cannot be predicted from its component parts. BERT’s training objectives give it access to a tremendous amount of lexico-semantic information, and while BERTology has shown that BERT captures certain important linguistic dimensions, there have been no studies exploring the extent to which BERT might have access to constructional information. In this work we design several probes and conduct extensive experiments to answer this question. Our results allow us to conclude that BERT does indeed have access to a significant amount of information, much of which linguists typically call constructional information. The impact of this observation is potentially far-reaching as it provides insights into what deep learning methods learn from text, while also showing that information contained in constructions is redundantly encoded in lexico-semantics.

CxGBERT: BERT meets Construction Grammar

We propose a novel transcription workflow which combines spoken term detection and humanin-the-loop, together with a pilot experiment. This work is grounded in an almost zero-resource scenario where only a few terms have so far been identified, involving two endangered languages. We show that in the early stages of transcription, when the available data is insufficient to train a robust ASR system, it is possible to take advantage of the transcription of a small number of isolated words in order to bootstrap the transcription of a speech collection.

Enabling Interactive Transcription in an Indigenous Community

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.

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

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. 


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