Temporal Validity Change Prediction (2024)

Abstract: Temporal validity is an important property of text that has many downstream applications, such as recommender systems, conversational AI, and user status tracking. Existing benchmarking tasks often require models to identify the temporal validity duration of a single statement. However, many data sources contain additional context, such as successive sentences in a story or posts on a social media profile. This context may alter the duration for which the originally collected statement is expected to be valid. We propose Temporal Validity Change Prediction, a natural language processing task benchmarking the capability of machine learning models to detect context statements that induce such change. We create a dataset consisting of temporal target statements sourced from Twitter and crowdsource corresponding context statements. We then benchmark a set of transformer-based language models on our dataset. Finally, we experiment with a multitasking approach to improve the state-of-the-art performance.

heading: 1 Introduction
text: Information is not impervious to time. Whether it be a post on a social media timeline like “I am going grocery shopping”, a statement like “Barack Obama is the president of the United States” in a knowledge repository, or an advertisement like “Ariana Grande concert in town this weekend”, sentences frequently contain inherently time-sensitive information. Consequently, readers have to reason over whether the statement is still current and accurate when they ingest the information. This property of a statement can be described as its temporal validity (Almquist and Jatowt, 2019; Hosokawa et al., 2023; Lynden et al., 2023).
Unlike event duration prediction (Pan et al., 2006; Gusev et al., 2011; Vempala et al., 2018; Ocal et al., 2024), where the goal is to evaluate how long certain events take to unfold, temporal validity
encompasses the contemporaneity and truthfulness of a statement at a given time. This means factors such as the statement creation time and the relative occurrence time of any information within it must also be considered. Similar to previous work in the growing field of temporal commonsense reasoning (Wenzel and Jatowt, 2023a; Jain et al., 2023), determining the temporal validity of a statement often relies on our prior commonsense understanding of the world due to underspecified temporal expressions. However, it can be difficult to accurately reason over this property when sentences actually belong to a larger content stream. For example, extracting a single post from a social media timeline or a single sentence from a book may remove important contextual information about the statement’s validity period.
The most frequently benchmarked task in the temporal validity domain requires models to estimate the duration for which a statement is valid in isolation (Almquist and Jatowt, 2019; Lynden et al., 2023), despite datasets often being created from streams of texts, such as WikiHow articles or blog posts. To introduce the contextual dimension to such models, we propose a new natural language processing task called Temporal Validity Change
1424
Prediction (TVCP), visualized in Figure 1. This task is focused on identifying context statements that impact the duration for which a target statement is expected to be valid.
TVCP can be used to benchmark or fine-tune temporal commonsense reasoning in machine learning models, an area in which even recent large language models (LLMs) still underperform (Bian et al., 2023; Jain et al., 2023). TVCP may also be linked to domains that are dependent on implicit temporal knowledge, such as narrative-, temporal information-, and temporal relation extraction (Verhagen et al., 2007, 2010; UzZaman et al., 2013; Santana et al., 2023). Moreover, it provides an additional signal for duration-based temporal validity classifiers and can be used to identify temporal context statements in future data collection processes for various tasks related to temporal validity. In this sense, TVCP is a strong driver for future research into context-aware temporal validity reasoning. Finally, TVCP-based classifiers could also improve model performance on various downstream tasks, such as the following.
Content Prioritization: Social media services like Twitter rely on recommender systems to prioritize the vast amount of content that their users produce. For certain content, such as time-sensitive breaking news, estimating its importance through engagement metrics may not sufficiently reflect its urgency. By considering the temporal validity (Takemura and Tajima, 2012; Koul et al., 2022), it is possible to better estimate which content should be urgently shown to users. TVCP can be used to leverage a stream of social media posts as possible context to better estimate the temporal validity duration of any previously observed post.
User Status Tracking: Similarly, the content of a user’s posts on social media could be utilized for other analytical or business purposes, such as predicting revenue streams (Asur and Huberman, 2010; Deng et al., 2011; Lassen et al., 2014; Lu et al., 2014) or identifying trends in a community’s or an individual user’s behaviour (Li et al., 2018; Abe et al., 2018; Shen et al., 2020). TVCP can identify posts that refer to previously observed time-sensitive information and detect chains of thought about topics that may not be self-contained.
Conversational AI: Foundation models, such as CHATGPT (Ouyang et al., 2022) and BARD (Manyika, 2023), could incorporate the temporal validity of statements provided by the user to keep track of knowledge that is still relevant to the con-
versation. Using TVCP, new messages can provide context for previously learned time-sensitive facts.
While we focus on consumer-driven downstream tasks, TVCP also generalizes to more structured language and statements, such as in knowledge repositories, news articles, or scientific literature.
Our main contributions are the following:
1. We define a novel natural language processing task (TVCP) that requires models to predict the impact of a context statement on a target statement’s temporal validity duration.
2. We build a dataset composed of time-sensitive target statements, as well as follow-up statements that provide context on the temporal validity duration of the target statements.
3. We evaluate the performance of a set of transformer-based language models (LMs) on our dataset, including LLMs such as GPT3.5-TURBO (Ouyang et al., 2022), GPT-4TURBO (Achiam et al., 2023), LLAMA 2 (Touvron et al., 2023), and MIXTRAL 8X7B (Jiang et al., 2024).
4. We propose an augmentation to the fine-tuning process that leverages temporal validity duration labels to improve the performance of the state-of-the-art classifier.

heading: 2 Related Work

heading: 2.1 Temporal Commonsense Reasoning
text: Temporal commonsense reasoning is considered one of several categories of commonsense reasoning (Storks et al., 2019; Bhargava and Ng, 2022). A major driver of research specifically into temporal common sense appears to have been the transformer architecture (Vaswani et al., 2017) and resulting LMs (Radford et al., 2018; Kenton and Toutanova, 2019). In recent years, several datasets that specifically aim to benchmark temporal commonsense understanding have been published (Zhou et al., 2019; Ning et al., 2020; Zhang et al., 2020; Qin et al., 2021; Zhou et al., 2021), while ROCSTORIES (Mostafazadeh et al., 2016) appears to be the only dataset focussing on this type of reasoning before the publication of the transformer architecture. Small adjustments to transformer-based LMs are often proposed as stateof-the-art solutions for these datasets (Pereira et al., 2020; Yang et al., 2020; Zhou et al., 2020; Pereira et al., 2021; Kimura et al., 2021; Zhou et al., 2021,
2022; Cai et al., 2022; Yu et al., 2022). Similarly, temporalized transformer models are popular solutions for tasks such as document dating or semantic change detection (Rosin and Radinsky, 2022; Rosin et al., 2022; Wang et al., 2023).
The temporal commonsense taxonomy defined by Zhou et al. (2019) is frequently referenced. It contains the five dimensions of duration (how long an event takes), temporal ordering (typical order of events), typical time (when an event happens), frequency (how often an event occurs) and stationarity (whether a state holds for a very long time or indefinitely).

heading: 2.2 Temporal Validity
text: Compared to temporal commonsense reasoning, temporal validity of text is a less well-researched field (Almquist and Jatowt, 2019; Lynden et al., 2023; Hosokawa et al., 2023). It effectively combines three dimensions of the taxonomy by Zhou et al. (2019): Stationarity, to reason about whether a statement contains time-sensitive information, typical time, to reason about when the timesensitive information occurs, and duration, to reason about how long the time-sensitive information takes to resolve. As shown in Equation 1, the temporal validity of a statement s at a time t is a binary value that defines whether the information in s is valid (true) at the given time.
TV(s,t)=    True if information in s is valid at t, False otherwise (1)
Note that by definition, stationary statements do not contain any time-sensitive information (e.g., “Japan lies in Asia”). As expected, the temporal validity of such a statement is constant for any timestamp t.

heading: 2.3 Related Tasks

heading: Moments of Change (MoC)
text: Our goal of identifying statements of a certain impact from a stream of texts draws some parallels to recent work in identifying MoCs based on content shared on online platforms. MoCs are most commonly escalations or changes in a user’s mood, often evaluated on mental health datasets (De Choudhury et al., 2016; Pruksachatkun et al., 2019; Tsakalidis et al., 2022). However, certain concepts, such as the content-agnostic extraction of user timelines (i.e., subsets of a user’s posting
history) and longitudinal modelling, are applicable to other domains. Hills et al. (2023) propose a framework for evaluating timeline extraction methods, based on whether the extracted timelines are dense in the desired ground truth feature. In the future, it would be possible to adapt this framework to evaluate different approaches for extracting timelines rich in temporal context statements. However, our desired change points are not clearly abstracted by measurable concepts such as emotion or post sentiment, which Hills et al. (2023) show to have a strong correlation with the presence of MoCs in mental health. In this work, we also evaluate individual context statements, rather than employing longitudinal modelling. We chose this design as our proposed task is already rather difficult to perform for LMs, and the combination of several context statements may lead to clashing signals, as can be seen in Figure 1.
Temporal Validity Duration Prediction (TVDP) TVDP is the primary existing task that is evaluated in temporal validity research (Takemura and Tajima, 2012; Almquist and Jatowt, 2019; Lynden et al., 2023). The goal is to estimate the duration for which a statement is valid, starting at the statement creation time. We formalize this task in Equation 2, where ts is the timestamp at which the statement s is created.
TVDP(s)=maxt≥ts{t | TV(s,t)=True} (2)
The TVDP task is useful for text sources such as social media, where information on the posting time of a statement is readily available and can be used to infer the timespan during which the statement is valid.
Temporal Validity Reassessment (TVR) In TVR, defined also as Temporal Natural Language Inference by Hosokawa et al. (2023), the goal is a reassessment of the temporal validity of a target statement (st), given additional context in the form of a follow-up statement (sf ). Formally, we define TVR in Equation 3 (SUO = supported, INV = invalidated, UNK = unknown), where TVc(s, t) is the temporal validity of a statement s at a time t given context c. The UNK class is assigned in cases where TVsf (st, tsf ) is neither clearly supported nor invalidated by the context.
TVR(st,sf )=    SUO TVsf (st,tsf )=True INV TVsf (st,tsf )=False
UNK TVsf (st,tsf )=Unclear
(3)
Unlike TVDP, this task format does not require an explicit temporal anchoring of the target statement to reason over its validity, making it particularly useful for downstream applications such as story understanding, wherein a larger text stream of individual statements is provided with no clear temporal anchoring of statements. We propose Temporal Validity Reassessment as a new name for this task moving forward, to scope and align it with other tasks in the temporal validity domain.

heading: 2.4 Comparison with Related Work
text: Table 1 shows the most closely related research within the temporal validity domain. Our dataset is based on the TVCP task, whereas previous work was based on the TVDP and TVR tasks.
A distinctive attribute is the text source and the resulting temporal validity duration bias. For example, sentences sourced from news or Wikipedia articles often appear to be valid for years or longer. On the other hand, image captions may only contain ongoing information for a few seconds. We decided to source our sentences from Twitter due to its alignment with our proposed use cases. Similar to Lynden et al. (2023), our collected statements tend to be valid for a few hours on average.
We follow recent research by evaluating our dataset using transformer-based LMs, whereas earlier approaches relied on methods such as support vector classifiers (SVC).
With the exception of COTAK (Lynden et al., 2023), datasets tend to be relatively small. As crowdsourcing is used in all datasets referenced in Table 1 to annotate text spans with commonsense information, the costs of dataset creation can
1Based on analysis of a sample. TVDP labels are not available for the full dataset.
quickly escalate. We also use crowdsourcing to create sample context statements, further restricting the overall size of our dataset.

heading: 3 Task
text: In the context of the previously described related tasks, we propose Temporal Validity Change Prediction (TVCP). Similar to TVR, we require st and sf as an input for classification and determine a ternary label that provides information about the impact of sf on st. However, while TVR can be considered a standalone temporal validity reasoning process, TVCP primarily provides an additional signal for estimating the temporal validity duration of a statement, and is best used in conjunction with TVDP and a downstream task format where explicit temporal anchors for target statements can be derived. Formally, we define TVCP in Equation 4 (DEC = decreased, UNC = unchanged, INC = increased), where TVDPc(s) is the temporal validity duration of a statement s given context c. Figure 2 shows an example comparing the three temporal validity tasks.
TVCP(st,sf )=    DEC TVDP(st)>TVDP sf (st) UNC TVDP(st)=TVDP sf (st)
INC TVDP(st)<TVDP sf (st)
(4)
Of note are the implicit semantic roles of st and sf . While sf acts as additional contextual information, any information that is newly introduced in sf should not be evaluated on its temporal validity. The goal of the task is exclusively to estimate the change in the temporal validity duration of st.
We find that temporal validity change generally occurs along two dimensions. The first dimension is implicit versus explicit change. For example, an appointment mentioned in a target statement may be declared postponed in the follow-up statement, which would be an explicit change. On the other hand, the author may instead note in a followup statement that the appointment is for a surgery, which may cause us to re-evaluate the duration of
the appointment, although the event itself has not changed. The second dimension is a change to the occurrence time versus the duration of the information. For example, a flight may be delayed, in which case the occurrence time changes. Alternatively, the flight might have to be re-routed mid-air due to bad weather, in which case the duration changes. Figure 3 shows the frequency of each combination of dimensions in a qualitative analysis of 200 context statements from our dataset. Generally, changes to the duration tend to be slightly more frequent than changes to the occurrence time. This makes sense, as context is less likely to change the occurrence time of already ongoing information, which constitutes a large part of our target statements.

heading: 4 Dataset
text: We create a dataset for training and benchmarking TVCP, where each sample is a quintuple <st,sf ,TVDP(st),TVDP sf (st),TVCP(st,sf )>.
st consists of posts sampled from Twitter. After retrieving tweets from the full-archive search API, we apply RegEx-based rules to remove tweets that may not be self-contained (e.g., indications of lists or threads). We also employ several LMs to detect spam, offensive content, and statements that are likely stationary. Any remaining tweets that do not contain temporal information are tagged during the crowdsourcing process and discarded from the final dataset. The full collection pipeline is explained in more detail in Appendix A. Our code, including all preprocessing steps, is published under the CC BY 4.0 licence (Wenzel and Jatowt, 2024).
We quantify three types of time-sensitive information. Actions (e.g., “I am baking bread”), events (e.g., “Job interview tomorrow”), and temporary states (e.g.,“It is nice out today”). We analyse the time-sensitive information in 100 target statements and visualize the result in Figure 4. Notably, onethird of sampled statements contain at least two distinct pieces of time-sensitive information with differing temporal validity spans, a trait that further distinguishes temporal validity research from event duration prediction. As previous work often focuses only on actions (Zhou et al., 2020; Hosokawa et al., 2023; Lynden et al., 2023), our dataset may contain more diverse temporal information in comparison.
For each target statement st, we ask two crowdworkers to estimate TVDP(st) from the logarithmic class design shown in Equation 5, which models uncertainty seen in humans when pinpointing distant events (Jatowt and Au Yeung, 2011; Varshney and Sun, 2013; Howard, 2018). If the annotators disagreed on the label, we supplied a third vote. We discarded any tweets that were annotated as no time-sensitive information, less than one minute or
more than one month, and tweets where no majority agreement could be reached. Table 2 shows the number of accepted and rejected statements, broken down by whether a third vote was supplied. The label distribution of accepted statements is shown in Figure 5.
Third Vote Accepted Rejected No 867 571 Yes 1,012 546
Temporal Validity Duration
Figure 5: Distribution of TVDP labels (before temporal validity change) in our dataset
Both sf and TVDPsf (st) were provided by a separate set of crowdworkers, given st and TVDP(st) as an input. Authors were asked to provide a statement sf that would cause the temporal validity duration of st to decrease, remain the same, or increase, respectively. The updated temporal validity duration label is provided by the same author, meaning the target TVCP label, which is derived from the duration labels, is guaranteed to match the author’s intent.
For each target statement, we provide one sample per TVCP class, thus ensuring a balanced dataset. Each sample consists of distinct sf , TVDPsf (st), and TVCP(st, sf ) labels. We collect 5,055 samples from 1,685 target statements, noting average word counts for st (16.1, σ = 4.40) and sf (14.6, σ = 3.29). Similar to Hosokawa et al. (2023), our crowdsourced context statements are on average shorter than the target statements and have
lower variance, likely due to participants aiming to replicate the form of the provided samples.
In Figure 6, we plot the temporal validity change delta, which is the class distance between the original and the updated TVDP estimate. We find that, in most cases, the temporal validity duration of a target statement is shifted into the adjacent class.
Crowdsourcing was conducted on Amazon Mechanical Turk. We split the crowdsourcing work into two distinct tasks: The annotation of target statements with a temporal validity duration estimate (TVDP), and the subsequent creation and annotation of context statements. Working on either task required participants to fulfil certain criteria, such as a long track record of previous work on the platform with a high acceptance rate. In addition, each task required participants to pass a multiple-choice qualification test. Finally, results were manually vetted per participant up to a certain threshold to ensure that participants were properly following the task descriptions. The full task setup for both crowdsourcing tasks is described in Appendix B. Other than the annotations, the only data we collect are sample counts and work times by each pseudonymized participant. We do not collect any personal or identifying data. We publish the resulting dataset for public use under the CC BY 4.0 licence (Wenzel and Jatowt, 2023b). In accordance with the Twitter developer policy2, we only publish the Tweet IDs of sourced statements.

heading: 5 Experiments

heading: 5.1 Language Models
text: The evaluated models include fine-tuned transformer-based LMs as well as LLMs prompted in a few-shot setting. We evaluate BERT (Kenton and Toutanova, 2019) and ROBERTA (Liu et al., 2019) as baseline models. SELFEXPLAIN (Sun et al., 2020), which achieved state-of-the-art results on the TNLI dataset, and still performs very
2https://developer.twitter.com/en/ developer-terms/policy, accessed 12.10.2023
competitively on datasets such as SST-5 (Socher et al., 2013) and SNLI (Bowman et al., 2015), represents a state-of-the-art transformer-based classification model. We initialize these models with pre-trained weights and fine-tune them on our dataset. For LLMs, we evaluate GPT-3.5-TURBO, GPT-4-TURBO, MIXTRAL 8X7B, and LLAMA 2 in a few-shot setting. Our prompt is based on chain-of-thought reasoning (Wei et al., 2022). Further, we follow the TELeR taxonomy (Santu and Feng, 2023) to the best of our abilities to create an appropriate prompt. Our prompt is single-turn and instruction-based with a defined system role, and contains a high-level general directive, bullet-list style subtasks, few-shot samples, and an explicit statement asking the LLM to explain its output. The evaluated models, their parameter counts (if publicised), and the evaluation modes are listed in Table 3. The training and prompting process is described in more detail in Appendix C. We use 42 as the random seed consistently throughout the initialization and training process of all fine-tuned models.
For all fine-tuned models, we also provide a multitask implementation, in which we add two regression layers that aim to respectively predict TVDP(st) and TVDPsf (st) from the same hidden representation. For these layers, we calculate the loss as the mean squared error between a single output neuron and a linear mapping of the TVDP class index to the range [0, 1]. Our intuition is that embeddings with an understanding of TVDP labels may be better suited for TVCP. Inspiration for this approach are models that utilize the interplay between temporal dimensions to improve their temporal commonsense reasoning performance, such as SYMTIME (Zhou et al., 2021) or SLEER (Cai et al., 2022). The number of trainable parameters added by this approach is negligible.

heading: 5.2 Quantitative Analysis

heading: Evaluation Metrics
text: We evaluate two metrics, accuracy and exact
match (EM). Accuracy is simply the fraction of correctly classified samples. EM is the fraction of target statements for which all three corresponding samples were correctly classified. This metric punishes inconsistency in the model more strictly, thus providing a better view of the true performance and task understanding of each model (Wenzel and Jatowt, 2023a), while disincentivizing shallow reasoning behaviours commonly seen in transformer models (Helwe et al., 2021; Tan et al., 2023).
We report the mean EM and accuracy across a five-fold cross-validation split. Each split consists of 70% training data, 10% validation data, and 20% test data. The results are shown in Table 4. In the remainder of this section, we refer to the bestperforming model, SELFEXPLAIN with multitask fine-tuning, as MULTITASK.

heading: Foundation Model Performance
text: In our evaluation, few-shot prompted foundation models consistently rank far below fine-tuned, smaller LMs, including simple baselines such as BERT and ROBERTA. This is consistent with previous research (Bian et al., 2023; Jain et al., 2023), which shows that temporal reasoning is an area in which foundation models are lacking. Another possible reason for the discrepancy is that the few-shot learning approach likely leads to a lack of knowledge about dataset-specific traits that a trained classifier could leverage.
LLAMA 2 in particular suffers from a high rate (26.07%) of explanations that violate the prompt by not providing one of the three target classes. This behaviour is not seen in other LLMs. Interestingly, while accuracy slightly increases for GPT-4-TURBO compared to GPT-3.5-TURBO, the EM score does not increase, indicating that there is no need to resort to exceedingly large models to achieve state-of-the-art few-shot results on TVCP.
When breaking down the classification accuracy by the temporal validity change delta (Figure 7), MULTITASK strongly outperforms GPT-
3.5-TURBO on both neutral (i.e., a change delta of 0) and non-neutral (i.e., a change delta of > 0) context statements. However, while the performance of MULTITASK remains relatively stable across all delta values, GPT-3.5-TURBO shows a decrease in accuracy on non-neutral context statements with a small temporal validity change delta.

heading: Multitasking
text: We note a positive impact on the EM score from implementing multitasking in all fine-tuned models. To measure the statistical significance of implementing multitask learning on SELFEXPLAIN, we use a technique known as bootstrapping. We resample from both classification sets 10,000 times with replacement and evaluate the fraction of resulting samples where MULTITASK outperforms SELFEXPLAIN. We find p = 0.0012 for accuracy, with a 95% confidence interval of [0.0036, 0.0192]. For EM, the significance is smaller at p = 0.0216, with a 95% confidence interval of [0.0006, 0.0397], due to the smaller number of samples, as EM is based on target statements.

heading: 5.3 Further Analysis and Experiments

heading: Data Quantity
text: To evaluate the impact of training data quantity on classifier performance, we train the MULTITASK classifier on a single train-val-test split (80%/10%/10%) with different amounts of training data. The results can be seen in Figure 8. At 100% of our training data, the model begins to telegraph a performance plateau, but does not yet appear to be saturated.

heading: Pre-Fine-Tuning
text: We experiment with pre-fine-tuning from other temporal commonsense tasks by comparing the performance of three BERT-based models. BERT-BASEUNCASED contains regular weights as learned during BERT’s pre-training, while the two variants BERT-TACOLM (Zhou et al., 2020) and BERTCOTAK (Lynden et al., 2023) each use weights
fine-tuned on their respective temporal commonsense datasets. The pre-fine-tuned weights for both models are published by the respective authors. We fine-tune the models from the published checkpoints on our dataset under the same conditions as our main evaluation. The mean accuracy and EM score for each model is listed in Table 5. Our evaluation shows that the use of weights fine-tuned on other temporal commonsense tasks does not seem to have a positive impact on the final TVCP performance of the model. It is possible that, although the resulting embeddings of models fine-tuned on temporal commonsense tasks are more aligned with temporal properties (Zhou et al., 2020), other important information in the embeddings is lost, leading to an overall decreased performance.

heading: Error Patterns
text: In Figure 9, we compare the confusion matrices between true labels and GPT-3.5-TURBO and MULTITASK, respectively. We see that in terms of relative performance, MULTITASK struggles more with distinguishing between the decreased and increased classes, whereas GPT-3.5-TURBO classifies a rather large amount of neutral statements as non-neutral, and vice versa.
Table 6 shows some advanced concepts of the TVCP task that current models struggle to properly evaluate. In the first sample, LMs take the author’s concern of being kicked out as a hint that the activity may be cut short. Here, it is important to remember that TVCP labels are always based on a change in the TVDP label of st. In this case, sf indicates that the activity of rooftop training has been ongoing for a long time already, and the overall duration of the activity is likely longer than we
would expect from just seeing st without context. In the second sample, although the context contains a temporal quantifier (“all week”), it does not impact the duration for which the author will be watching the show. LMs sometimes overvalue temporal quantifiers in context statements, leading to incorrect classifications.
The final sample shows an example of ambiguity in temporal validity durations. The context statement clearly states the planned time at which the action in st will take place, but the TVCP label can differ based on the initial interpretation of st. During crowdsourcing, our participants denoted st as being likely to take place later on the same day. In contrast, GPT-3.5-TURBO reasons that st will take place in the following 1-3 days, leading to the predicted unchanged label.
These advanced concepts of temporal validity reasoning not only require more robust and expressive LMs, but may also require a labelling scale that can express ideas like uncertainty and ambiguity, as it may even be hard for humans to pinpoint a single appropriate temporal validity duration for them. The quantification of such error patterns and subsequent approaches to solving them provide an interesting direction for future work.

heading: 6 Conclusion and Future Work
text: In this work, we have introduced TVCP, a task designed to reason over the impact of context on a statement’s temporal validity duration. While TVCP is mainly intended as a driver for future work, we propose downstream applications by leveraging its output as a classification signal for TVDP. We provide a benchmark dataset for our task, as well as a set of baseline evaluation results. We find that the performance of fine-tuned classifiers can be consistently improved by explicitly incorporating TVDP labels as a loss signal. Despite the impressive feats performed by foundation models, we report, similar to previous work (Bian et al., 2023; Jain et al., 2023), poor performance in the temporal commonsense domain. These findings show that users should carefully evaluate whether an LLM properly understands a given task before choosing it over smaller, fine-tuned models. We also show that models pre-fine-tuned on existing temporal commonsense tasks do not necessarily lead to better performance on TVCP. Finally, we highlight several advanced concepts of temporal validity that may be difficult to reason over for both LMs and human annotators.
Future work could involve using TVCP-based classifiers to collect a larger number of temporal context statements. A comparison of context-aware TVDP classifiers with previous models (Almquist and Jatowt, 2019) could emphasize the importance of accurate semantic segmentation between targetand context statements. Further, the use of our dataset for generative approaches could be explored, for example, in the context of generative adversarial networks. Currently, we only evaluate the impact of single context statements on a specific target statement. In the future, longitudinal timeline modelling, as applied in MoC, could be appropriated to reason over larger sections of a user’s posting history. For our multitasking implementation, directions for future work could be changes to hyperparameters such as the weight of the auxiliary loss, changes to the definition of the auxiliary task (e.g., log-scaled regression or ordinal classification), or even entirely new auxiliary tasks. In the realm of LLMs, further experiments with different zero-shot or few-shot prompting strategies are also feasible. Finally, research into models differentiating temporal and stationary information could significantly enhance the development of future temporal validity research.

heading: Limitations
text: Although we focus on creating a reproducible training- and evaluation environment, some variables are out of our control. For example, bit-wise reproducibility is only guaranteed on the same CUDA toolkit version and when executed on a GPU with the same architecture and the same number of streaming multiprocessors. This means that an exact reproduction of the models discussed in this article may not be possible. Nevertheless, we expect trends to remain the same across GPU architectures.
One of the major limitations of our approach is likely the dataset size. Although a relatively small dataset size is common in temporal commonsense reasoning, we find that our model performance still increases with the amount of training data used. The existing synthesized context statements in our dataset could be used to bootstrap an approach for automatically extracting additional samples from social media to alleviate this issue.
The data we collect is not personal in nature. However, the possibility of latent demographic biases in our data exists, for example, regarding certain language structures or expressions used in the creation of follow-up statements. This could lead to the propagation of any such bias when the dataset is used to bootstrap further data collection, which should be considered in future work.
Our external validity is mainly threatened by two factors. First, our context statements are crowdsourced. While we apply several steps to ensure the produced context is sensible, it is unclear whether context on certain platforms, such as on social media, manifests in similar structures as in our dataset, with respect to traits such as sentence length, grammaticality, and phrasing.
Second, similar to how pre-training weights from other temporal commonsense tasks do not seem to improve the classifier performance on our dataset, the weights generated as part of our training process are likely very task-specific, and may not generalize well to other tasks or text sources.
Overall, we recommend the use of the TVCP dataset and resulting classifiers for bootstrapping further research into combining the duration- and inference-based temporal validity tasks, as well as research into directly predicting updated temporal validity durations and improving the generalizability to different text sources, rather than for a direct downstream task application.

heading: A Twitter Collection Pipeline
text: To collect candidate tweets, we query the Twitter full-archive search API with the following parameters:
− p l a c e _ c o u n t r y : KP l a n g : en − i s : r e t w e e t − i s : r e p l y − i s : q u o t e − has : h a s h t a g s − has : c a s h t a g s − has : l i n k s − has : m e n t i o n s − has : media − has : images − has : v i d e o _ l i n k
Essentially, our goal is to collect English candidate tweets that are self-contained. This means we discard tweets that refer to other tweets (replies, mentions, retweets, or quote retweets), tweets that contain media that might provide external context (such as videos, images, links, or other types of media), and tweets that contain Twitter-specific features (hashtags, cashtags). Since Twitter’s API does not allow queries based only on these conditions, we add a constraint stating that the source country of the tweet may not be North Korea, to minimize the impact on the generalizability of our target statements.
From these collected tweets, we drop duplicates and then perform basic preprocessing, including the removal of emojis, non-ASCII characters, and excess whitespace characters. As a sanity check,
we remove any remaining tweets that contain http , @, or #. Most such tweets will already have been filtered by the API query, so not many tweets are lost in this step. We then apply a set of filtering steps, which are summarized in Figure 10, and are explored further in the remainder of this section.

heading: A.1 Syntactic Filtering
text: We first filter tweets by length, removing tweets with less than 25 or more than 200 characters. We also remove tweets containing question marks, after noting in our initial inspection that questions often have ambiguous temporal validity durations that depend on the dialogue (e.g., a question might no longer be considered temporally relevant after it has been answered). We leave such special cases to future work. We remove tweets starting with a period ( .), which often manifests as ... , as well as tweets starting with a comma (,), as they indicate that a tweet may be a continuation of a prior context. Similarly, tweets ending with a colon (:) indicate that there is a dependence on some type of following context that we have not captured.

heading: A.2 Semantic Filtering
text: We devise a set of RegEx rules to capture specific patterns, such as recurring word repetitions, which were often associated with a small set of nonsensical spam in our sample dataset, such as “CAN WE GO BACK CAN WE GO BACK CAN WE GO BACK”. Another RegEx string is responsible for detecting tweets containing phrases such as “Good morning” or “Have a nice Monday”. These kinds of expressions are commonly found on Twitter, and, while they technically fit the task description, it is unlikely that the temporal validity duration of such a statement would be changed. Thus, we remove them from our candidate statements to increase the diversity and authenticity of generated follow-
up statements. Other RegEx strings aim to capture common representations of threads or list iterations, such as “1/3)” or “[2/x]”. Additionally, we remove tweets with more than 25% quoted text from the dataset. We find that such tweets often either consist of full quotes (often associated with figures of speech), or statements where the temporal validity of the quote is at odds with that of the rest of the statement.

heading: A.3 Content-Based Filtering
text: To filter offensive content, we use a binary classification model based on ROBERTA, which can be downloaded via the Huggingface transformers library under the name “cardiffnlp/twitter-robertabase-offensive”. We also use the word-list-based better-profanity Python library3. To minimize offensive content in our dataset, we only keep tweets that do not contain any profanity from the word list and that are considered non-offensive by the transformer model. To filter out additional spam, we use a BERT-TINY-based binary classification model trained on SMS spam, which can be found under the name “mrm8488/bert-tiny-finetuned-smsspam-detection” in the Huggingface transformers library. We find that most items have a relatively low probability of being spam under this model, with the upper fence being roughly 0.07. Thus, we choose 0.07 as a cut-off point, removing all samples that exceed this probability. While not very sophisticated, this model works well for detecting texts containing hashes, Bitcoin addresses, or other types of data that do not fit into our task description.

heading: A.4 Model-Based Ranking
text: We apply an ensemble of models based on the previously mentioned COTAK (Lynden et al., 2023) and ALMQUIST2019 (Almquist and Jatowt, 2019) datasets. The authors of the COTAK dataset publish their model for estimating action effect durations, which we retrieve from the Huggingface transformers library under the name “mrfriedpotato/effect”. For the ALMQUIST2019 dataset, we train a neural network based on the pooler output of BERT-BASE-UNCASED, initialized with epoch 2 weights from TACOLM (Zhou et al., 2020). The model parameters are frozen, while a single linear layer predicting the output class is trained with a dropout probability of 0.1. ADAMW (Loshchilov and Hutter, 2018) was used as an optimizer with a
3https://pypi.org/project/better-profanity/
learning rate of 1e− 5. The corresponding model achieved 0.74 Micro-F1 and 0.69 Macro-F1 on an 80-10-10 train-val-test split after just a few epochs, outperforming non-transformer-based methods proposed in the original paper.
We rank statements by the averaged probability that they are not classified as longer (than days) in the COTAK-based model, and are not classified as months or years in the ALMQUIST2019-based model. A statement that either model classifies as one of the classes mentioned above is automatically assigned a score of -1. For all other statements, the softmax scores of the remaining classes are summed up, and the average of the two summed probabilities is used as the score. We then sort the resulting dataset by this score and prioritize the highest-scoring statements for crowdsourcing.

heading: B Crowdsourcing Setup
text: In this section, we provide details on the crowdsourcing implementation. As noted, we use Amazon Mechanical Turk to collect crowdsourced data from participants.

heading: B.1 Temporal Validity Duration Estimation
text: We assume the average layman is not familiar with the term temporal validity. Thus, we define the task as “determining how long the information within the tweet remains relevant after its publication”, i.e., for how long the user would consider the tweet timely and relevant. We provide the option no timesensitive information to tag any stationary statements that were not removed during preprocessing. The task is otherwise a relatively straightforward classification task. We split our dataset into batches of 10 samples that are grouped into a single human intelligence task (HIT). For each HIT, we offer a compensation of USD0.25, based on an estimated 6-9 seconds of processing time per individual statement (i.e., 60-90 seconds per HIT). Figures 11 to 14 show the crowdsourcing task layout.

heading: B.2 Follow-Up Content Generation
text: Compared to the temporal validity duration estimation task, the follow-up content generation task requires a much more robust understanding of the overall concept of temporal validity and the respective semantic roles of the target- and follow-up statements. Hence, we focus on providing a more detailed explanation of the task. Figures 15 to 17 show the crowdsourcing task layout. The detailed
instructions tab is not listed due to its length, but contains instructions that can also be found in the code repository as part of the qualification test. Notably, we labelled the target statement as the context tweet in this crowdsourcing task to emphasize that participants should not alter this statement directly, as this was a problem that occurred somewhat frequently during pilot tests. This contrasts with our formal definition of TVCP, where providing context is the role of the follow-up statement.
Each HIT requires participants to provide three follow-up statements, one for each TVCP class (DEC, UNC, INC), as well as the corresponding updated TVDP labels. For each HIT, we offer a compensation of USD0.35. We base our compensation on an estimated 30–40 seconds of processing time per follow-up statement (i.e., 90–120 seconds per HIT) due to the creative writing involved.

heading: B.3 Discouraging Dishonest Activity
text: In initial pilot runs, we find that many submissions are the result of spam, dishonest activity, or a complete lack of task understanding, with many provided annotations being inexplicable by common sense. To increase the quality of work on both tasks, we introduced three measures.
First, we required participants to have an overall approval rate of 90% on the platform, as well as 1,000 approved HITs. Without these requirements, the amount of blatant spam (e.g., copy-pasted content) increases significantly.
Second, we devised qualification tests for both tasks. Participants had to determine the temporal validity durations for a set of sample statements to work on the temporal validity duration estimation task, and determine the correctness of followup statements and their updated duration labels to work on the follow-up content generation task.
Finally, we vet all participants’ responses individually up to a certain threshold. For each task, we manually verify the first 20 submissions of each annotator on their quality. We provide feedback and manually adapt submissions when they are partially incorrect. If submission quality is appropriate by the time a participant reaches 20 submitted HITs, we consider them as trusted, and only spot-check every 5th submission thereafter. If submission quality does not sufficiently improve at this point, we prohibit the participant from further working on the task.

heading: C Evaluation Setup

heading: C.1 Fine-Tuning Strategy
text: We perform several experiments to improve the model setup for our fine-tuned baselines, BERT and ROBERTA. First, for each model, we evaluate two separate pipelines. The TRANSFORMERCLASSIFIER pipeline concatenates both statements of a sample before embedding them jointly, whereas the SIAMESECLASSIFIER (Bromley et al., 1993; Nandy et al., 2020) pipeline generates a separate embedding for the target- and context statement, and combines them to form a hidden representation [hst , hsf , hst − hsf , hst ⊗ hsf ].
We perform hyperparameter testing regarding the dropout probability before the classification layer (0.1, 0.25, 0.5), the base learning rate (1e-2, 1e-3, 1e-4), and freezing the layers of the embedding model (freeze, nofreeze). For both BERT and ROBERTA in the freeze and nofreeze setting, we perform grid search over the learning rate and dropout probability.
For both hyperparameter optimization and model training, we use the ADAMW optimizer with ε = 1e-8, β1 = 0.9, β2 = 0.999, weight_decay = 0.01 and optimize for cross-entropy loss. If the validation EM score does not exceed the best previously observed value for 5 consecutive epochs, we stop training early. The model epoch with the best validation EM score is used for evaluating the test set. For hyperparameter testing, we use a predefined train-val-test split (80%/10%/10%) rather than fivefold cross-validation.
Table 7 shows the three best-performing configurations for BERT and ROBERTA in the freeze and nofreeze settings, respectively, on the TRANSFORMERCLASSIFIER pipeline. Table 8 shows the same results for the SIAMESECLASSIFIER pipeline.
The most notable finding appears to be that ROBERTA gets stuck in a false minimum of predicting a constant class when embedding layers are unfrozen, leading to an accuracy of 0.33 and an EM of 0. Hence, we freeze embedding layers for ROBERTA in our main evaluation. ROBERTAbased models with frozen embedding layers tend to have a worse baseline performance, but have a higher relative improvement when switching to the SIAMESECLASSIFIER implementation. We hypothesize that ROBERTA’s sentence embedding token, 
, may contain less information about the full sequence than BERT’s [SEP] token, due to
the lack of a next-sentence-prediction task during pre-training.
For SELFEXPLAIN, we use the originally proposed learning rate of 2e− 5 and no dropout. The evaluation setup is otherwise identical. The final layer of all models before classification has a dimensionality of 768. All models were trained and evaluated on an MSI GeForce RTX 3080 GAMING X TRIO 10G GPU using CUDA 11.7. Training and evaluation of the models, as well as hyperparameter tests, took around 15 GPU hours.

heading: C.2 Few-Shot Prompting Strategy
text: For models evaluated via few-shot prompting, we first provide the following system prompt:
“You are a language model specialized in reasoning over temporal common sense. You know that the temporal validity duration of a statement is the duration for which said statement contains relevant and current information after its creation. Information that takes place in the future, such as "I will take a shower at 8 p.m.", is considered valid from the point of state-
ment creation until the information has fully resolved.
Your task is to determine the impact of a context statement on the temporal validity duration of a target statement. The user will provide both statements. When a statement can be interpreted in multiple ways, assume the most likely interpretation is the correct one.
To solve the task effectively, follow the steps outlined below:
1. Ignoring the context statement, determine the temporal validity duration of the target statement. Your estimate must match one of the following labels: [less than one minute, 1-5 minutes, 5-15 minutes, 15-45 minutes, 45 minutes - 2 hours, 2-6 hours, more than 6 hours, 1- 3 days, 3-7 days, 1-4 weeks, more than one month]. Select exactly one class and explain why it is the most fitting.
2. Once again, determine the most likely of the above labels to match the temporal validity duration of the target statement, but this time, include any information from the context statement that may influence the class label. Similar to step 1, explain why you chose the class.
3. Compare the two class labels generated in step 1 and step 2 to infer the change in the temporal validity duration. Select the appropriate class label [decreased, unchanged, increased], depending on if the introduction of the context statement decreased, remained neutral to, or increased the temporal validity duration of the target statement. You must surround this class label with backticks (‘).”
After the system prompt, we provide nine sample conversation turns, each consisting of a user prompt and a corresponding sample response by the system. For each TVCP class, we provide three samples. Each sample classification begins with the following user prompt.
“Please determine the impact of the context statement on the temporal validity duration of the target statement.
Target Statement: [st] Context Statement: [sf ]”
Afterwards, an explanation is provided by the assistant role. All few-shot samples are listed below, sorted by their target class. In the actual prompt, we apply a fixed perturbation to the order of fewshot samples to avoid recency bias in the model responses. Zhao et al. (2021) show that when all samples of a certain class are placed at the end of a few-shot prompt, this might encourage the model to assign said class more frequently. The temperature of the models is set to 0 for all generative experiments.
Target Statement: “My sister: We should go out for a drink. Just ONE. Us: *end up getting drunk*” Context Statement: “Just found out I have an early morning meeting tomorrow” Explanation: “The target statement suggests a night out, which has already started. Such an occasion may still continue for several hours after the statement was made. For this reason, the most fitting label is 2-6 hours. The context statement notes an early morning meeting on the following day. Upon finding out about this meeting, it is likely that the author would cut their night out short. Although it is not clear how much time has passed between the two statements, a reasonable assumption is that the author would discover such a meeting and subsequently cease the activity within the next 15-45 minutes. For this reason, the most fitting label is 15-45 minutes. As the information in the context statement decreases the temporal validity duration of the target statement from 2-6 hours to 15-45 minutes, the final label is ‘decreased‘”
Target Statement: “Cab was early, so here I am at Midway with an hour to spare. And get Dunkin’ Donuts does not actually have any donuts.” Context Statement: “I might just take the earlier train then, it’s a flex ticket anyway.” Explanation: “The target statement states that the author has an hour to spare at Midway. For this reason, the most fitting label is 45 minutes - 2 hours. The context statement specifies that the author could be taking an earlier train, reducing the amount of time they have to spare at Midway. As they do not seem to be in a hurry, the most fitting updated label is 15-45 minutes. As the information in the context statement decreases the temporal validity duration of the target statement from 45
minutes - 2 hours to 15-45 minutes, the final label is ‘decreased‘”
Target Statement: “I have 1 extra LA Bakeshop Cheesebread /Spanish Bread Mix. DM if you want it.” Context Statement: “Whoops, sorry. . . gone in seconds.” Explanation: “The target statement contains an offer regarding some free bread mix. Depending on the reach of the author, it is likely that someone would be interested in this offer relatively quickly. For this reason, the most fitting label is 15-45 minutes. The context statement states that the offer was taken within seconds. If this statement can be taken at face value, it suggests the most fitting label is less than one minute. As the information in the context statement decreases the temporal validity duration of the target statement from 15-45 minutes to less than one minute, the final label is ‘decreased‘”
Target Statement: “taxi going home. back to reality.” Context Statement: “How can a week long vacation feel like a single day. . . ?” Explanation: “The target statement suggests that the author is on a taxi ride home. The typical duration of a taxi ride may be between 15-45 minutes. For this reason, the most fitting label is 15-45 minutes. The context statement provides additional information about where the author is coming from, and about the duration of their vacation. However, the duration of the taxi ride is unaffected by this information. For this reason, the most fitting label is still 15-45 minutes. As the information in the context statement does not change the temporal validity duration of the target statement, the final label is ‘unchanged‘”
Target Statement: “I swear I woke up with the mindset to have a great day” Context Statement: “Meditation really seems to be working. . . maybe I should stick with it” Explanation: “The target statement refers to a mindset regarding the current day. As a result, the most fitting label is More than 6 hours. The context statement reveals meditation as a possible cause for the author’s current mindset. The author contemplates sticking with meditation to improve their mood over a longer duration. However, the context statement is not clear on if the author will
follow through on this thought, and the process of meditation is not necessarily linked to the author’s long-term feelings. For this reason, the most fitting label is still More than 6 hours. As the information in the context statement does not change the temporal validity duration of the target statement, the final label is ‘unchanged‘”
Target Statement: “BTW I’m playing splatoon almost all day, come poke me” Context Statement: “I can play this game for hours. Maybe I should go pro” Explanation: “According to the target statement, the author plans on playing the game Splatoon almost all day. For this reason, the most fitting label is More than 6 hours. In the context statement, the author further professes their enjoyment for the game and contemplates possible future plans. However, this information is not likely to impact the duration for which the playsession in question will continue. For this reason, the most fitting label is still More than 6 hours. As the information in the context statement does not change the temporal validity duration of the target statement, the final label is ‘unchanged‘”
Target Statement: “I’m watching Perfect Match and I feel like this chic Francesca is so selflish” Context Statement: “Why did they make her the focus of the double runtime special???” Explanation: “Perfect Match is a TV series with a typical episode runtime between 38-65 minutes. According to the target statement, the author has already started watching the episode, so some of the episode’s runtime has likely already passed. For this reason, the most fitting label is 15-45 minutes. The context statement notes that the episode is a double runtime special. In this case, it is likely that the remaining runtime exceeds 45 minutes. For this reason, the most fitting label is 45 minutes - 2 hours. As the information in the context statement increases the temporal validity duration of the target statement from 15-45 minutes to 45 minutes - 2 hours, the final label is ‘increased‘”
Target Statement: “I’m about to read some tech news really quick then enjoy dinner.” Context Statement: “Oh, I forgot we had a reservation at that fancy dinner place later today. . . I guess I’ll get by with chips for now” Explanation: “Reading tech news and subse-
quently enjoying dinner, as suggested by the target statement, is likely to take longer than 45 minutes, but unlikely to take longer than 2 hours. For this reason, the most fitting label is 45 minutes - 2 hours. The context reveals that the author has a dinner reservation later in the day, and will only be eating chips for the time being. Since the dinner is already mentioned in the target statement, it remains valid until this event takes place. As the reservation takes place later in the day, the most fitting label is 2-6 hours. As the information in the context statement increases the temporal validity duration of the target statement from 45 minutes - 2 hours to 2-6 hours, the final label is ‘increased‘”
Target Statement: “just patiently waiting for my bff to get here” Context Statement: “He’ll be flying in from the states next Monday. So excited!!!” Explanation: “The target statement states that the author is waiting on the arrival of their bff. With no further information, it is reasonable this refers to an imminent situation. For this reason, the most fitting label is 15-45 minutes. The context statement reveals that the author’s BFF is in fact flying in from abroad in the following week, thus, the event is not imminent. Since the context statement mentions this event takes place on the next Monday, it is not possible for more than 7 full days to pass before this event occurs. As a result, the most fitting label is 3-7 days. As the information in the context statement increases the temporal validity duration of the target statement from 15-45 minutes to 3-7 days, the final label is ‘increased‘”

heading: Future Work
text: Information that takes place in the future, such as "I will take a shower at 8 p. m. ", is considered valid from the point of state-
ment creation until the information has fully resolved. Your task is to determine the impact of a context statement on the temporal validity duration of a target statement. The user will provide both statements. When a statement can be interpreted in multiple ways, assume the most likely interpretation is the correct one. To solve the task effectively, follow the steps outlined below:
1. Ignoring the context statement, determine the temporal validity duration of the target statement. Your estimate must match one of the following labels: [less than one minute, 1-5 minutes, 5-15 minutes, 15-45 minutes, 45 minutes - 2 hours, 2-6 hours, more than 6 hours, 1- 3 days, 3-7 days, 1-4 weeks, more than one month]. Select exactly one class and explain why it is the most fitting. 2. Once again, determine the most likely of the above labels to match the temporal validity duration of the target statement, but this time, include any information from the context statement that may influence the class label. Similar to step 1, explain why you chose the class. 3. Compare the two class labels generated in step 1 and step 2 to infer the change in the temporal validity duration. Select the appropriate class label [decreased, unchanged, increased], depending on if the introduction of the context statement decreased, remained neutral to, or increased the temporal validity duration of the target statement. You must surround this class label with backticks (‘). ”
After the system prompt, we provide nine sample conversation turns, each consisting of a user prompt and a corresponding sample response by the system. For each TVCP class, we provide three samples. Each sample classification begins with the following user prompt. “Please determine the impact of the context statement on the temporal validity duration of the target statement. Target Statement: [st] Context Statement: [sf ]”
Afterwards, an explanation is provided by the assistant role. All few-shot samples are listed below, sorted by their target class. In the actual prompt, we apply a fixed perturbation to the order of fewshot samples to avoid recency bias in the model responses. Zhao et al. (2021) show that when all samples of a certain class are placed at the end of a few-shot prompt, this might encourage the model to assign said class more frequently. The temperature of the models is set to 0 for all generative experiments. Target Statement: “My sister: We should go out for a drink. Just ONE. Us: *end up getting drunk*” Context Statement: “Just found out I have an early morning meeting tomorrow” Explanation: “The target statement suggests a night out, which has already started. Such an occasion may still continue for several hours after the statement was made. For this reason, the most fitting label is 2-6 hours. The context statement notes an early morning meeting on the following day. Upon finding out about this meeting, it is likely that the author would cut their night out short. Although it is not clear how much time has passed between the two statements, a reasonable assumption is that the author would discover such a meeting and subsequently cease the activity within the next 15-45 minutes. For this reason, the most fitting label is 15-45 minutes. As the information in the context statement decreases the temporal validity duration of the target statement from 2-6 hours to 15-45 minutes, the final label is ‘decreased‘”
Target Statement: “Cab was early, so here I am at Midway with an hour to spare. And get Dunkin’ Donuts does not actually have any donuts. ” Context Statement: “I might just take the earlier train then, it’s a flex ticket anyway. ” Explanation: “The target statement states that the author has an hour to spare at Midway. For this reason, the most fitting label is 45 minutes - 2 hours. The context statement specifies that the author could be taking an earlier train, reducing the amount of time they have to spare at Midway. As they do not seem to be in a hurry, the most fitting updated label is 15-45 minutes. As the information in the context statement decreases the temporal validity duration of the target statement from 45
minutes - 2 hours to 15-45 minutes, the final label is ‘decreased‘”
Target Statement: “I have 1 extra LA Bakeshop Cheesebread /Spanish Bread Mix. DM if you want it. ” Context Statement: “Whoops, sorry. gone in seconds. ” Explanation: “The target statement contains an offer regarding some free bread mix. Depending on the reach of the author, it is likely that someone would be interested in this offer relatively quickly. For this reason, the most fitting label is 15-45 minutes. The context statement states that the offer was taken within seconds. If this statement can be taken at face value, it suggests the most fitting label is less than one minute. As the information in the context statement decreases the temporal validity duration of the target statement from 15-45 minutes to less than one minute, the final label is ‘decreased‘”
Target Statement: “taxi going home. back to reality. ” Context Statement: “How can a week long vacation feel like a single day. ?” Explanation: “The target statement suggests that the author is on a taxi ride home. The typical duration of a taxi ride may be between 15-45 minutes. For this reason, the most fitting label is 15-45 minutes. The context statement provides additional information about where the author is coming from, and about the duration of their vacation. However, the duration of the taxi ride is unaffected by this information. For this reason, the most fitting label is still 15-45 minutes. As the information in the context statement does not change the temporal validity duration of the target statement, the final label is ‘unchanged‘”
Target Statement: “I swear I woke up with the mindset to have a great day” Context Statement: “Meditation really seems to be working. maybe I should stick with it” Explanation: “The target statement refers to a mindset regarding the current day. As a result, the most fitting label is More than 6 hours. The context statement reveals meditation as a possible cause for the author’s current mindset. The author contemplates sticking with meditation to improve their mood over a longer duration. However, the context statement is not clear on if the author will
follow through on this thought, and the process of meditation is not necessarily linked to the author’s long-term feelings. For this reason, the most fitting label is still More than 6 hours. As the information in the context statement does not change the temporal validity duration of the target statement, the final label is ‘unchanged‘”
Target Statement: “BTW I’m playing splatoon almost all day, come poke me” Context Statement: “I can play this game for hours. Maybe I should go pro” Explanation: “According to the target statement, the author plans on playing the game Splatoon almost all day. For this reason, the most fitting label is More than 6 hours. In the context statement, the author further professes their enjoyment for the game and contemplates possible future plans. However, this information is not likely to impact the duration for which the playsession in question will continue. For this reason, the most fitting label is still More than 6 hours. As the information in the context statement does not change the temporal validity duration of the target statement, the final label is ‘unchanged‘”
Target Statement: “I’m watching Perfect Match and I feel like this chic Francesca is so selflish” Context Statement: “Why did they make her the focus of the double runtime special???” Explanation: “Perfect Match is a TV series with a typical episode runtime between 38-65 minutes. According to the target statement, the author has already started watching the episode, so some of the episode’s runtime has likely already passed. For this reason, the most fitting label is 15-45 minutes. The context statement notes that the episode is a double runtime special. In this case, it is likely that the remaining runtime exceeds 45 minutes. For this reason, the most fitting label is 45 minutes - 2 hours. As the information in the context statement increases the temporal validity duration of the target statement from 15-45 minutes to 45 minutes - 2 hours, the final label is ‘increased‘”
Target Statement: “I’m about to read some tech news really quick then enjoy dinner. ” Context Statement: “Oh, I forgot we had a reservation at that fancy dinner place later today. I guess I’ll get by with chips for now” Explanation: “Reading tech news and subse-
quently enjoying dinner, as suggested by the target statement, is likely to take longer than 45 minutes, but unlikely to take longer than 2 hours. For this reason, the most fitting label is 45 minutes - 2 hours. The context reveals that the author has a dinner reservation later in the day, and will only be eating chips for the time being. Since the dinner is already mentioned in the target statement, it remains valid until this event takes place. As the reservation takes place later in the day, the most fitting label is 2-6 hours. As the information in the context statement increases the temporal validity duration of the target statement from 45 minutes - 2 hours to 2-6 hours, the final label is ‘increased‘”
Target Statement: “just patiently waiting for my bff to get here” Context Statement: “He’ll be flying in from the states next Monday. So excited!!!” Explanation: “The target statement states that the author is waiting on the arrival of their bff. With no further information, it is reasonable this refers to an imminent situation. For this reason, the most fitting label is 15-45 minutes. The context statement reveals that the author’s BFF is in fact flying in from abroad in the following week, thus, the event is not imminent. Since the context statement mentions this event takes place on the next Monday, it is not possible for more than 7 full days to pass before this event occurs. As a result, the most fitting label is 3-7 days. As the information in the context statement increases the temporal validity duration of the target statement from 15-45 minutes to 3-7 days, the final label is ‘increased‘”