Search for: All records

A plant species list was created for Niwot Ridge and Green Lakes Valley from species identified in those areas by NWT scientists, working primarily at the Saddle and Martinelli sites. Additions to this list included species identified by Komarkova (1979) in the Indian Peaks Wilderness area but not on Niwot Ridge or in the Green Lakes Valley because of the likelihood that those species might exist within the LTER research area. Additions to the list were also provided by Terry Theodose, Leeanne Lestak, Teresa Nettleton, Susan Sherrod, Laura Mujica-Crapanzano (2004), Hope Humphries (2006), and Jane G. Smith (2019-2025). The list was revised to remove duplicate entries, correct typos, and resolve synonymy problems. Species and non-species categories received USDA PLANTS database names and codes. 

Permanent 1 m^2 vegetation plots were established near each of the 88 Saddle grid stakes in 1989 by Marilyn Walker, who led the sampling effort until 1997. To estimate plant canopy cover, point quadrat measurements have been made at irregular intervals from 1989 to the present (1989, 1990, 1995, 1997, 2006, 2008 and yearly from 2010 onward). The point-quadrat technique used for sampling was described in Spasojevic et al. (2013) and Auerbach (1992). Auerbach, N. 1992. Effects of road and dust disturbance in minerotrophic and acidic tundra ecosystems, northern Alaska. University of Colorado, Boulder, Colorado, USA. Spasojevic, Marko J, William D Bowman, Hope C Humphries, Timothy R Seastedt, and Katharine N Suding. Changes in alpine vegetation over 21 years: Are patterns across a heterogeneous landscape consistent with predictions?” Ecosphere 4, no. 9 (2013): 1–18. https://doi.org/10.1890/es13-00133.1. 

Total aboveground live vascular biomass was clipped from 50x20 cm plots in areas near the saddle grid permanent plots on Niwot Ridge to measure net primary productivity. NDVI measurements were also included in some years. 

Educational dialogue systems have been used to support students and teachers for decades. Such systems rely on explicit pedagogically motivated dialogue rules. With the ease of integrating large language models (LLMs) into dialogue systems, applications have been arising that directly use model responses without the use of human-written rules, raising concerns about their use in classroom settings. Here, we explore how to constrain LLM outputs to generate appropriate and supportive teacher-like responses. We present results comparing the effectiveness of different constraint variations in a zero-shot prompting setting on a large mathematics classroom corpus. Generated outputs are evaluated with human annotation for Fluency, Relevance, Helpfulness, and Adherence to the provided constraints. Including all constraints in the prompt led to the highest values for Fluency and Helpfulness, and the second highest value for Relevance. The annotation results also demonstrate that the prompts that result in the highest adherence to constraints do not necessarily indicate higher perceived scores for Fluency, Relevance, or Helpfulness. In a direct comparison, all of the non-baseline LLM responses were ranked higher than the actual teacher responses in the corpus over 50% of the time. 

Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years. While we have seen progress with generating syntactically correct utterances that preserve semantics, various shortcomings of NNLG systems are clear: new tasks require new training data which is not available or straightforward to acquire, and model outputs are simple and may be dull and repetitive. This paper addresses these two critical challenges in NNLG by: (1) scalably (and at no cost) creating training datasets of parallel meaning representations and reference texts with rich style markup by using data from freely available and naturally descriptive user reviews, and (2) systematically exploring how the style markup enables joint control of semantic and stylistic aspects of neural model output. We present YelpNLG, a corpus of 300,000 rich, parallel meaning representations and highly stylistically varied reference texts spanning different restaurant attributes, and describe a novel methodology that can be scalably reused to generate NLG datasets for other domains. The experiments show that the models control important aspects, including lexical choice of adjectives, output length, and sentiment, allowing the models to successfully hit multiple style targets without sacrificing semantics. 

Effective storytelling relies on engagement and interaction. This work develops an automated software platform for telling stories to children and investigates the impact of two design choices on children’s engagement and willingness to interact with the system: story distribution and the use of complex gesture. A storyteller condition compares stories told in a third person, narrator voice with those distributed between a narrator and first-person story characters. Basic gestures are used in all our storytellings, but, in a second factor, some are augmented with gestures that indicate conversational turn changes, references to other characters and prompt children to ask questions. An analysis of eye gaze indicates that children attend more to the story when a distributed storytelling model is used. Gesture prompts appear to encourage children to ask questions, something that children did, but at a relatively low rate. Interestingly, the children most frequently asked “why” questions. Gaze switching happened more quickly when the story characters began to speak than for narrator turns. These results have implications for future agent-based storytelling system research. 

Question Generation is the task of automatically creating questions from textual input. In this work we present a new Attentional Encoder–Decoder Recurrent Neural Network model for automatic question generation. Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels. The linguistic features are designed to capture information related to named entity recognition, word case, and entity coreference resolution. In addition our model uses a copying mechanism and a special answer signal that enables generation of numerous diverse questions on a given sentence. Our model achieves state of the art results of 19.98 Bleu 4 on a benchmark Question Generation dataset, outperforming all previously published results by a significant margin. A human evaluation also shows that the added features improve the quality of the generated questions. 

Responses in task-oriented dialogue systems often realize multiple propositions whose ultimate form depends on the use of sentence planning and discourse structuring operations. For example a recommendation may consist of an explicitly evaluative utterance e.g. Chanpen Thai is the best option, along with content related by the justification discourse relation, e.g. It has great food and service, that combines multiple propositions into a single phrase. While neural generation methods integrate sentence planning and surface realization in one endto-end learning framework, previous work has not shown that neural generators can: (1) perform common sentence planning and discourse structuring operations; (2) make decisions as to whether to realize content in a single sentence or over multiple sentences; (3) generalize sentence planning and discourse relation operations beyond what was seen in training. We systematically create large training corpora that exhibit particular sentence planning operations and then test neural models to see what they learn. We compare models without explicit latent variables for sentence planning with ones that provide explicit supervision during training. We show that only the models with additional supervision can reproduce sentence planning and discourse operations and generalize to situations unseen in training. 

Conversational systems typically focus on functional tasks such as scheduling appointments or creating todo lists. Instead we design and evaluate SlugBot (SB), one of 8 semifinalists in the 2018 AlexaPrize, whose goal is to support casual open-domain social inter-action. This novel application requires both broad topic coverage and engaging interactive skills. We developed a new technical approach to meet this demanding situation by crowd-sourcing novel content and introducing playful conversational strategies based on storytelling and games. We collected over 10,000 conversations during August 2018 as part of the Alexa Prize competition. We also conducted an in-lab follow-up qualitative evaluation. Over-all users found SB moderately engaging; conversations averaged 3.6 minutes and involved 26 user turns. However, users reacted very differently to different conversation subtypes. Storytelling and games were evaluated positively; these were seen as entertaining with predictable interactive structure. They also led users to impute personality and intelligence to SB. In contrast, search and general Chit-Chat induced coverage problems; here users found it hard to infer what topics SB could understand, with these conversations seen as being too system-driven. Theoretical and design implications suggest a move away from conversational systems that simply provide factual information. Future systems should be designed to have their own opinions with personal stories to share, and SB provides an example of how we might achieve this. 

Natural language generators for task-oriented dialog should be able to vary the style of the output utterance while still effectively realizing the system dialog actions and their associated semantics. While the use of neural generation for training the response generation component of conversational agents promises to simplify the process of producing high quality responses in new domains, to our knowledge, there has been very little investigation of neural generators for task-oriented dialog that can vary their response style and we know of no experiments on models that can generate responses that are different in style from those seen during training, while still maintaining semantic fidelity to the input meaning representation. Here, we show that a model that is trained to achieve a single stylistic personality target can produce outputs that combine stylistic targets. We carefully evaluate the multivoice outputs for both semantic fidelity and for similarities to and differences from the linguistic features that characterize the original training style. We show that contrary to our predictions, the learned models do not always simply interpolate model parameters, but rather produce styles that are distinct and novel from the personalities they were trained on.