Narrative analysis is becoming increasingly important for a number of linguistic tasks including summarization, knowledge extraction, and question answering. We present a novel approach for narrative event representation using attention to re-context
ualize events across the whole story. Comparing to previous analysis we find an unexpected attachment of event semantics to predicate tokens within a popular transformer model. We test the utility of our approach on narrative completion prediction, achieving state of the art performance on Multiple Choice Narrative Cloze and scoring competitively on the Story Cloze Task.
Extracting temporal information is critical to process health-related text. Temporal information extraction is a challenging task for language models because it requires processing both texts and numbers. Moreover, the fundamental challenge is how to
obtain a large-scale training dataset. To address this, we propose a synthetic data generation algorithm. Also, we propose a novel multi-task temporal information extraction model and investigate whether multi-task learning can contribute to performance improvement by exploiting additional training signals with the existing training data. For experiments, we collected a custom dataset containing unstructured texts with temporal information of sleep-related activities. Experimental results show that utilising synthetic data can improve the performance when the augmentation factor is 3. The results also show that when multi-task learning is used with an appropriate amount of synthetic data, the performance can significantly improve from 82. to 88.6 and from 83.9 to 91.9 regarding micro-and macro-average exact match scores of normalised time prediction, respectively.
When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the
narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
This paper presents StoryDB --- a broad multi-language dataset of narratives. StoryDB is a corpus of texts that includes stories in 42 different languages. Every language includes 500+ stories. Some of the languages include more than 20 000 stories.
Every story is indexed across languages and labeled with tags such as a genre or a topic. The corpus shows rich topical and language variation and can serve as a resource for the study of the role of narrative in natural language processing across various languages including low resource ones. We also demonstrate how the dataset could be used to benchmark three modern multilanguage models, namely, mDistillBERT, mBERT, and XLM-RoBERTa.
Over the past decade, the field of natural language processing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection. While this work has brought an impo
rtant empirical lens for examining narrative, it is by and large divorced from the large body of theoretical work on narrative within the humanities, social and cognitive sciences. In this position paper, we introduce the dominant theoretical frameworks to the NLP community, situate current research in NLP within distinct narratological traditions, and argue that linking computational work in NLP to theory opens up a range of new empirical questions that would both help advance our understanding of narrative and open up new practical applications.
Narrative generation is an open-ended NLP task in which a model generates a story given a prompt. The task is similar to neural response generation for chatbots; however, innovations in response generation are often not applied to narrative generatio
n, despite the similarity between these tasks. We aim to bridge this gap by applying and evaluating advances in decoding methods for neural response generation to neural narrative generation. In particular, we employ GPT-2 and perform ablations across nucleus sampling thresholds and diverse decoding hyperparameters---specifically, maximum mutual information---analyzing results over multiple criteria with automatic and human evaluation. We find that (1) nucleus sampling is generally best with thresholds between 0.7 and 0.9; (2) a maximum mutual information objective can improve the quality of generated stories; and (3) established automatic metrics do not correlate well with human judgments of narrative quality on any qualitative metric.
Understanding narrative text requires capturing characters' motivations, goals, and mental states. This paper proposes an Entity-based Narrative Graph (ENG) to model the internal- states of characters in a story. We explicitly model entities, their i
nteractions and the context in which they appear, and learn rich representations for them. We experiment with different task-adaptive pre-training objectives, in-domain training, and symbolic inference to capture dependencies between different decisions in the output space. We evaluate our model on two narrative understanding tasks: predicting character mental states, and desire fulfillment, and conduct a qualitative analysis.
Arab criticism has accessed a phase in which the tendency to changing the diction
of dealing with the text whether it was narrative or verse was an urgent necessity.
The most important aspects of this change are shown in the interest in what is
ca
lled (Form), the non-separation between the content and the context, revealing the
internal secrets of texts in an attempt to refreshing the point of view to the narrative text
and dealing in a different way with its structure throughout making a reading variety
derived from the common and the uncommon modern critical theories.
The interest in studying narrative structure on top of the critical researches
involved in novel which take part in introducing critical processing that analyses texts and
aims to uncover them away from mental – social and historical references that go-round
them.
Al-Wahidi is one of the greatest syntactic critics who have explained al-Mutanabbi's Anthology. His explanation contains concepts, and syntactic and critical opinions that deserve study and scrutiny. Al-Mutanabbi's poetry stands as a fertile domain f
or syntractic
criticism as is apparent in the critical arena over his poetry. Through his syntactic judgement, al-Wahidi attempts to support a doctrine, oppose some point of view, elaborate on what violates a proposed principle, or uncover a certain problematic issue somewhere in
al-Mutanabbi's poetry; specially when disagreement among critics' opinions appears, and dissimilarity among their doctrines and approaches materialises. Poetry was and is still one
significant source to formulate the syntactic structure, even if it witnessed some unstability due to narrators' uncertainties, and imprecision of transference. So, narratives and narrators
of poetry have varied, which has consequently created an obvious phenomenon that requires research, and study of the effect that may have on the syntactic rules. This is because syntax is one fundamental aspect of the culture of those interested in the literatry
exegeses. This study comes to focus on one essential aspect of syntactic criticism that is already applied to al-Mutanabbi's poetry.
In his poem "Torture of AL_ Hallag", AL_Bayati tries to benefit
of the narrative techniques since they are not only characteristic of the
novel, but of lingual discovers in general, and a system of lingual practice
which we can notice in many ling
ual genres. Using narrative techniques
in poems appears through narrative tyres because a poem maintains its
basis of harmony, description , imagination and so on .
These techniques in the text tend to more to the dramatic feature,
in which, voices and characters vary, and the narrative feature appears to
be a standard on which the poet constructs his text, and derives his ideas
and visions . He gets benefits of historic events through their existence in
order to fulfill creative writing , which in turn, produces real verse which
intensities, the narrative in a way that suits the poetic students .
Through this , the narrative structures contributes to building the poetic
text but do not totally affects them nor appear in a clear from which
undermine the poem . This means, it comes through the necessary
elements which contribute and fortify the poem's creativity , and elevate
it twards a space of beauty which might be more open to other types of
discourse .
