The author is a computational linguist based in Thailand. He is a retired former associate professor of linguistics at Georgetown University, and also formerly a principal scientist at Yahoo Labs. His books include The Imagined Moment (2010) and Computational Modeling of Narrative (2012), and he has also published numerous papers and short stories.When robots read books
Artificial intelligence sheds new light on classic texts. Literary theorists who don’t embrace it face obsolescence
Where do witches come from, and what do those places have in common? While browsing a large collection of traditional Danish folktales, the folklorist Timothy Tangherlini and his colleague Peter Broadwell, both at the University of California, Los Angeles, decided to find out. Armed with a geographical index and some 30,000 stories, they developed WitchHunter, an interactive ‘geo-semantic’ map of Denmark that highlights the hotspots for witchcraft.
The system used artificial intelligence (AI) techniques to unearth a trove of surprising insights. For example, they found that evil sorcery often took place close to Catholic monasteries. This made a certain amount of sense, since Catholic sites in Denmark were tarred with diabolical associations after the Protestant Reformation in the 16th century. By plotting the distance and direction of witchcraft relative to the storyteller’s location, WitchHunter also showed that enchantresses tend to be found within the local community, much closer to home than other kinds of threats. ‘Witches and robbers are human threats to the economic stability of the community,’ the researchers write. ‘Yet, while witches threaten from within, robbers are generally situated at a remove from the well-described village, often living in woods, forests, or the heath … it seems that no matter how far one goes, nor where one turns, one is in danger of encountering a witch.’
Such ‘computational folkloristics’ raise a big question: what can algorithms tell us about the stories we love to read? Any proposed answer seems to point to as many uncertainties as it resolves, especially as AI technologies grow in power. Can literature really be sliced up into computable bits of ‘information’, or is there something about the experience of reading that is irreducible? Could AI enhance literary interpretation, or will it alter the field of literary criticism beyond recognition? And could algorithms ever derive meaning from books in the way humans do, or even produce literature themselves?
Computer science isn’t as far removed from the study of literature as you might think. Most contemporary applications of AI consist of sophisticated methods for learning patterns, often through the creation of labels for large, unwieldy data-sets based on structures that emerge from within the data itself. Similarly, not so long ago, examining the form and structure of a work was a central focus of literary scholarship. The ‘structuralist’ strand of literary theory tends to deploy close – sometimes microscopic – readings of a text to see how it functions, almost like a closed system. This is broadly known as a ‘formal’ mode of literary interpretation, in contrast to more historical or contextual ways of reading.
The so-called ‘cultural’ turn in literary studies since the 1970s, with its debt to postmodern understandings of the relationship between power and narrative, has pushed the field away from such systematic, semi-mechanistic ways of analysing texts. AI remains concerned with formal patterns, but can nonetheless illuminate key aspects of narrative, including time, space, characters and plot.
Consider the opening sentence of Gabriel García Márquez’s One Hundred Years of Solitude (1967): ‘Many years later, as he faced the firing squad, Colonel Aureliano Buendía was to remember that distant afternoon when his father took him to discover ice.’ The complex way in which Márquez represents the passage of time is a staple of modern fiction. The time corresponding to ‘Many years later’ includes the fateful time of ‘facing’ the firing squad, which in turn is simultaneous with that final ‘remember’-ing, which is years after ‘that distant afternoon’. In a single sentence, Márquez paints a picture of events in the fleeting present, memories of the past and visions for the future.
According to numerous psychological studies, when we read such stories, we construct timelines. We represent to ourselves whether events are mentioned before, after or simultaneous with each other, and how far apart they are in time. Likewise, AI systems have also been able to learn timelines for a variety of narrative texts in different languages, including news, fables, short stories and clinical narratives.
In most cases, this analysis involves what’s known as ‘supervised’ machine learning, in which algorithms train themselves from collections of texts that a human has laboriously labelled. Timeframes in narratives can be represented using a widely used annotation standard called TimeML (which I helped to develop). Once a collection (or ‘corpus’) of texts is annotated and fed into an AI program, the system can learn rules that let it accurately identify the timeline in other new texts, including the passage from Márquez. TimeML can also measure the tempo or pace of the narrative, by analysing the relationship between events in the text and the time intervals between them.
AI annotation schemes are versatile and expressive, but they’re not foolproof*First they came for the journalists and I did not speak out-
The presence of narrative ‘zigzag’ movements in fiction is one of the intriguing insights to emerge from this kind of analysis. It’s evident in this passage from Marcel Proust’s posthumously published novel Jean Santeuil (1952), the precursor to his magnum opus In Search of Lost Time (1913-27):
Sometimes passing in front of the hotel he remembered the rainy days when he used to bring his nursemaid that far, on a pilgrimage. But he remembered them without the melancholy that he then thought he would surely some day savour on feeling that he no longer loved her.The narrative here oscillates between two poles, as the French structuralist critic Gérard Genette observed in Narrative Discourse (1983): the ‘now’ of the recurring events of remembering while passing in front of the hotel, and the ‘once’ or ‘then’ of the thoughts remembered, involving those rainy days with his nursemaid.
Even though AI annotation schemes are versatile and expressive, they’re not foolproof. Longer, book-length texts are prohibitively expensive to annotate, so the power of the algorithms is restricted by the quantity of data available for training them. Even if this tagging were more economical, machine-learning systems tend to fare better on simpler narratives and on relating events that are mentioned closer together in the text. The algorithms can be foxed by scene-setting descriptive prose, as in this sentence from Honoré de Balzac’s novella Sarrasine (1831), in which the four states being described should (arguably) overlap with each other:
The trees, being partly covered with snow, were outlined indistinctly against the greyish background formed by a cloudy sky, barely whitened by the moon.AI criticism is also limited by the accuracy of human labellers, who must carry out a close reading of the ‘training’ texts before the AI can kick in. Experiments show that readers tend to take longer to process events that are distant in time or separated by a time shift (such as ‘a day later’). Such processing creates room for error, although distributing standard annotation guidelines to users can reduce it. People also have a hard time imagining temporally complex situations, such as the mind-bending ones described in Alan Lightman’s novel Einstein’s Dreams (1992):
For in this world, time has three dimensions, like space. … Each future moves in a different direction of time. Each future is real. At every point of decision, whether to visit a woman in Fribourg or to buy a new coat, the world splits into three worlds, each with the same people, but different fates for those people. In time, there are an infinity of worlds.Spotting temporal patterns might be fun and informative, but isn’t literature more than the sum of the information lurking in its patterns? Of course, there might be phenomenological aspects of storytelling that remain ineffable, including the totality of the work itself. Even so, literary interpretation is often an inferential process. It requires sifting through and comparing chunks of information about literature’s form and context – from the text itself, from its historical and cultural background, from authorial biographies, critiques and social-media reactions, and from the reader’s prior experience. All of this is data, and eminently minable....MUCH MORE
Because I was not a journalist.
Then they came for the ad agency creatives and I did not speak out-
Because I was not an ad agency creative. (see below)
Then they came for the financial analysts and I
said 'hang on one effin minute'.
A Novel Co-Authored By An Artificial Intelligence Program Longlisted For Japanese SciFi Literary Prize
Artificial Intelligence Algos To Read, Understand and Comment On News Stories
This seems efficient.
Combine the story-writing programs* with the commenting programs and just cut the humans out completely.